Applications of Machine Learning in Human Microbiome Studies: A Review on Feature Selection, Biomarker Identification, Disease Prediction and Treatment

Predicting Bacterial Vaginosis Development using Artificial Neural Networks

Bacterial vaginosis (BV) is a dysbiosis of the vaginal microbiome, characterized by the depletion of protective Lactobacillus spp. and overgrowth of anaerobes. Artificial neural network (ANN) modeling of vaginal microbial communities offers an opportunity for early detection of incident BV (iBV). 16S rRNA gene sequencing and quantitative PCR was performed on longitudinal vaginal specimens collected from participants within 14 days of iBV or from healthy participants to calculate the inferred absolute abundance (IAA) of vaginal bacterial taxa. ANNs were trained using the IAA of vaginal taxa from 420 vaginal specimens to classify individual vaginal specimens as either pre-iBV (collected before iBV onset) or Healthy. Feature importance was assessed to understand how specific vaginal micro-organisms contributed to model predictions. ANN modeling accurately classified >97% of specimens as either pre-iBV or Healthy (sensitivity >96%, specificity >98%) using IAA of 20 vaginal taxa. Model prediction accuracy was maintained when training models using only a few key vaginal taxa. Models trained using only the top five most important features achieved an accuracy of >97%, sensitivity >92%, and specificity >99%. Model predictive accuracy was further improved by training models on specimens from white and black participants separately; using only three feature models achieved an accuracy >96%, sensitivity >91%, and specificity >91%. Feature analysis found that Lactobacillus species L. gasseri and L. jensenii differed in how they contributed to model predictions in models trained with data stratified by race. A total of 420 vaginal specimens were analyzed, providing a robust dataset for model training and validation.

Importance

Bacterial vaginosis (BV) is the most common vaginal infection and is associated with numerous comorbidities. BV is associated with infertility, preterm birth, pelvic inflammatory disease, and increased risk of HIV/STI acquisition. BV is difficult to detect prior to onset, and infection commonly recurs after treatment. Our model allows for the accurate early detection of iBV by surveying the vaginal microbiome, potentially serving as a valuable tool to determine which patients are at risk of developing iBV. Early detection of iBV could lead to wider adoption of clinical interventions useful in the prevention of iBV such as live biotherapeutics, prophylactic antibiotics, and/or behavioral modifications. Our findings indicate that few microbial targets are required for accurate predictions, facilitating cost and time effective clinical testing. Similarly, our study highlights the value of developing models personalized to specific patient populations, improving accuracy while reducing the number of taxa required for accurate predictions.

Impact of Blastocystis carriage and colonization intensity on gut microbiota composition in a non-westernized rural population from Colombia

BACKGROUND

The role of Blastocystis, a common intestinal parasitic protist of humans and other animals, in human health and disease remains elusive. Recent studies suggest a connection between Blastocystis colonization, healthier lifestyles, and high-diversity gut microbiota. Nevertheless, studies concerning the relationship between Blastocystis colonization, its intensity, and gut microbiota composition -involving both bacterial and eukaryotic communities- remain limited.

METHODS

This study examines the impact of Blastocystis carriage and colonization intensity on gut microbiota composition in a rural community in Colombia. A total of 88 human samples were collected from the rural population of Las Guacas village, located in the Cauca department in southwest Colombia. We utilized 16S and 18S rDNA sequencing to analyze both bacterial and eukaryotic microbiota, comparing Blastocystis-positive and -negative individuals, as well as groups with varying Blastocystis colonization intensity (low, medium, high), to identify distinct microbiota profiles and differentially abundant taxa linked to each condition.

RESULTS

The analysis revealed significant differences between Blastocystis-positive and -negative individuals. In terms of bacterial composition and structure, Blastocystis-positive individuals exhibited distinct microbiota profiles, as shown by beta diversity analysis. Taxa associated with colonization included Bacteroides, Prevotella, Oscillibacter, Faecalibacterium, and Alistipes. Higher Blastocystis colonization intensity was associated with an increased abundance of taxa such as Alistipes and Lachnospira, while lower intensities correlated with beneficial bacteria such as Akkermansia. Regarding eukaryotic composition, beta diversity analysis revealed distinct profiles associated with Blastocystis colonization. Differentially abundant taxa, including Entamoeba coli, were more prevalent in Blastocystis-positive individuals, while Blastocystis-negative individuals exhibited a higher abundance of opportunistic fungi, such as Candida albicans. Machine learning models, including random forest classifiers, supported these findings, identifying Faecalibacterium and Bacteroides as predictors of Blastocystis colonization.

CONCLUSIONS

These findings suggest that Blastocystis may modulate gut microbiota, contributing to microbial balance providing new insights into the ecological implications of Blastocystis in rural populations.

Recent advances in therapeutic probiotics: insights from human trials

SUMMARYRecent advances in therapeutic probiotics have shown promising results across various health conditions, reflecting a growing understanding of the human microbiome's role in health and disease. However, comprehensive reviews integrating the diverse therapeutic effects of probiotics in human subjects have been limited. By analyzing randomized controlled trials (RCTs) and meta-analyses, this review provides a comprehensive overview of key developments in probiotic interventions targeting gut, liver, skin, vaginal, mental, and oral health. Emerging evidence supports the efficacy of specific probiotic strains and combinations in treating a wide range of disorders, from gastrointestinal (GI) and liver diseases to dermatological conditions, bacterial vaginosis, mental disorders, and oral diseases. We discuss the expanding understanding of microbiome-organ connections underlying probiotic mechanisms of action. While many clinical trials demonstrate significant benefits, we acknowledge areas requiring further large-scale studies to establish definitive efficacy and optimal treatment protocols. The review addresses challenges in standardizing probiotic research methodologies and emphasizes the importance of considering individual variations in microbiome composition and host genetics. Additionally, we explore emerging concepts such as the oral-gut-brain axis and future directions, including high-resolution microbiome profiling, host-microbe interaction studies, organoid models, and artificial intelligence applications in probiotic research. Overall, this review offers a comprehensive update on the current state of therapeutic probiotics across multiple domains of human health, providing insights into future directions and the potential for probiotics to revolutionize preventive and therapeutic medicine.

Advanced computational tools, artificial intelligence and machine-learning approaches in gut microbiota and biomarker identification

The microbiome of the gut is a complex ecosystem that contains a wide variety of microbial species and functional capabilities. The microbiome has a significant impact on health and disease by affecting endocrinology, physiology, and neurology. It can change the progression of certain diseases and enhance treatment responses and tolerance. The gut microbiota plays a pivotal role in human health, influencing a wide range of physiological processes. Recent advances in computational tools and artificial intelligence (AI) have revolutionized the study of gut microbiota, enabling the identification of biomarkers that are critical for diagnosing and treating various diseases. This review hunts through the cutting-edge computational methodologies that integrate multi-omics data-such as metagenomics, metaproteomics, and metabolomics-providing a comprehensive understanding of the gut microbiome's composition and function. Additionally, machine learning (ML) approaches, including deep learning and network-based methods, are explored for their ability to uncover complex patterns within microbiome data, offering unprecedented insights into microbial interactions and their link to host health. By highlighting the synergy between traditional bioinformatics tools and advanced AI techniques, this review underscores the potential of these approaches in enhancing biomarker discovery and developing personalized therapeutic strategies. The convergence of computational advancements and microbiome research marks a significant step forward in precision medicine, paving the way for novel diagnostics and treatments tailored to individual microbiome profiles. Investigators have the ability to discover connections between the composition of microorganisms, the expression of genes, and the profiles of metabolites. Individual reactions to medicines that target gut microbes can be predicted by models driven by artificial intelligence. It is possible to obtain personalized and precision medicine by first gaining an understanding of the impact that the gut microbiota has on the development of disease. The application of machine learning allows for the customization of treatments to the specific microbial environment of an individual.

Metagenomics studies in aquaculture systems: Big data analysis, bioinformatics, machine learning and quantum computing

The burgeoning field of aquaculture has become a pivotal contributor to global food security and economic growth, presently surpassing capture fisheries in aquatic animal production as evidenced by recent statistics. However, the dense fish populations inherent in aquaculture systems exacerbate abiotic stressors and promote pathogenic spread, posing a risk to sustainability and yield. This study delves into the transformative potential of metagenomics, a method that directly retrieves genetic material from environmental samples, in elucidating microbial dynamics within aquaculture ecosystems. Our findings affirm that metagenomics, bolstered by tools in big data analytics, bioinformatics, and machine learning, can significantly enhance the precision of microbial assessment and pathogen detection. Furthermore, we explore quantum computing's emergent role, which promises unparalleled efficiency in data processing and model construction, poised to address the limitations of conventional computational techniques. Distinct from metabarcoding, metagenomics offers an expansive, unbiased profile of microbial biodiversity, revolutionizing our capacity to monitor, predict, and manage aquaculture systems with high accuracy and adaptability. Despite the challenges of computational demands and variability in data standardization, this study advocates for continued technological integration, thereby fostering resilient and sustainable aquaculture practices in a climate of escalating global food requirements.

Advancing Gut Microbiome Research: The Shift from Metagenomics to Multi-Omics and Future Perspectives

The gut microbiome, a dynamic and integral component of human health, has co-evolved with its host, playing essential roles in metabolism, immunity, and disease prevention. Traditional microbiome studies, primarily focused on microbial composition, have provided limited insights into the functional and mechanistic interactions between microbiota and their host. The advent of multi-omics technologies has transformed microbiome research by integrating genomics, transcriptomics, proteomics, and metabolomics, offering a comprehensive, systems-level understanding of microbial ecology and host-microbiome interactions. These advances have propelled innovations in personalized medicine, enabling more precise diagnostics and targeted therapeutic strategies. This review highlights recent breakthroughs in microbiome research, demonstrating how these approaches have elucidated microbial functions and their implications for health and disease. Additionally, it underscores the necessity of standardizing multi-omics methodologies, conducting large-scale cohort studies, and developing novel platforms for mechanistic studies, which are critical steps toward translating microbiome research into clinical applications and advancing precision medicine.

Machine Learning and Artificial Intelligence in the Multi-Omics Approach to Gut Microbiota

The gut microbiome is involved in human health and disease, and its comprehensive understanding is necessary to exploit it as a diagnostic or therapeutic tool. Multi-omics approaches, including metagenomics, metatranscriptomics, metabolomics, and metaproteomics, enable depiction of the gut microbial ecosystem's complexity. However, these tools generate a large data stream in which integration is needed to produce clinically useful readouts, but, in turn, might be difficult to carry out with conventional statistical methods. Artificial intelligence and machine learning have been increasingly applied to multi-omics datasets in several conditions associated with microbiome disruption, from chronic disorders to cancer. Such tools have potential for clinical implementation, including discovery of microbial biomarkers for disease classification or prediction, prediction of response to specific treatments, and fine-tuning of microbiome-modulating therapies. The state of the art, potential, and limits, of artificial intelligence and machine learning in the multi-omics approach to gut microbiome are discussed.

Operationalizing Team Science at the Academic Cancer Center Network to Unveil the Structure and Function of the Gut Microbiome

Oncologists increasingly recognize the microbiome as an important facilitator of health as well as a contributor to disease, including, specifically, cancer. Our knowledge of the etiologies, mechanisms, and modulation of microbiome states that ameliorate or promote cancer continues to evolve. The progressive refinement and adoption of "omic" technologies (genomics, transcriptomics, proteomics, and metabolomics) and utilization of advanced computational methods accelerate this evolution. The academic cancer center network, with its immediate access to extensive, multidisciplinary expertise and scientific resources, has the potential to catalyze microbiome research. Here, we review our current understanding of the role of the gut microbiome in cancer prevention, predisposition, and response to therapy. We underscore the promise of operationalizing the academic cancer center network to uncover the structure and function of the gut microbiome; we highlight the unique microbiome-related expert resources available at the City of Hope of Comprehensive Cancer Center as an example of the potential of team science to achieve novel scientific and clinical discovery.

Prediction of colorectal cancer based on taxonomic levels of microorganisms and discovery of taxonomic biomarkers using the Grouping-Scoring-Modeling (G-S-M) approach

Colorectal cancer (CRC) is one of the most prevalent forms of cancer globally. The human gut microbiome plays an important role in the development of CRC and serves as a biomarker for early detection and treatment. This research effort focuses on the identification of potential taxonomic biomarkers of CRC using a grouping-based feature selection method. Additionally, this study investigates the effect of incorporating biological domain knowledge into the feature selection process while identifying CRC-associated microorganisms. Conventional feature selection techniques often fail to leverage existing biological knowledge during metagenomic data analysis. To address this gap, we propose taxonomy-based Grouping Scoring Modeling (G-S-M) method that integrates biological domain knowledge into feature grouping and selection. In this study, using metagenomic data related to CRC, classification is performed at three taxonomic levels (genus, family and order). The MetaPhlAn tool is employed to determine the relative abundance values of species in each sample. Comparative performance analyses involve six feature selection methods and four classification algorithms. When experimented on two CRC associated metagenomics datasets, the highest performance metric, yielding an AUC of 0.90, is observed at the genus taxonomic level. At this level, 7 out of top 10 groups (Parvimonas, Peptostreptococcus, Fusobacterium, Gemella, Streptococcus, Porphyromonas and Solobacterium) were commonly identified for both datasets. Moreover, the identified microorganisms at genus, family, and order levels are thoroughly discussed via refering to CRC-related metagenomic literature. This study not only contributes to our understanding of CRC development, but also highlights the applicability of taxonomy-based G-S-M method in tackling various diseases.

Potential postmortem microbial biomarkers of infant and younger children death investigation

Microbial communities associated with the human body are highly dynamic and reflect the host environment and lifestyle over time. Studies show death is no exception, with data demonstrating similar antemortem and postmortem microbiomes up to 48 h following death. These predictable microbial biomarkers can inform death investigation by helping to estimate the postmortem interval and build models to identify cause and manner of death. However, no attempts have been made to model potential microbial biomarkers in pediatric (≤2 years) deaths. This study provided a cross-sectional survey of the microbiota of 53 pediatric cases (black, white, both sexes) seen in Wayne County, Michigan. Autopsy cases represented accidents, homicides, or natural causes. Postmortem microbiome were collected by swabbing the eyes, ears, nose, mouth, umbilicus, brain, rectum, trabecular space, and cardiac blood. 16S rRNA sequence analyses indicated that sex, race, age, body site, and manner of death (MOD) had significant effects on microbiome composition, with significant interactions among MOD, race, and age. Amplicon sequence variants identified intra- and interhost dispersion of the postmortem microbiome depending on death circumstance. Among manners of death, non-accidental deaths were significantly distinct from all other deaths, and among body sites the rectum was distinct in its microbial composition. There is a real need for robust postmortem microbiome before it can be standardized as a practical tool for use in forensic investigation or public health. These results inform postmortem microbial variability during pediatric death investigation that contributes to a larger effort to understand the postmortem microbiome.

Microbiota and metabolomic profiling coupled with machine learning to identify biomarkers and drug targets in nasopharyngeal carcinoma

Background

Nasopharyngeal carcinoma (NPC) is a prevalent malignancy in certain regions, with radiotherapy as the standard treatment. However, resistance to radiotherapy remains a critical challenge, necessitating the identification of novel biomarkers and therapeutic targets. The tumor-associated microbiota and metabolites have emerged as potential modulators of radiotherapy outcomes.

Methods

This study included 22 NPC patients stratified into radiotherapy-responsive (R, n = 12) and radiotherapy-non-responsive (NR, n = 10) groups. Tumor tissue and fecal samples were subjected to 16S rRNA sequencing to profile microbiota composition and targeted metabolomics to quantify short-chain fatty acids (SCFAs). The XGBoost algorithm was applied to identify microbial taxa associated with radiotherapy response, and quantitative PCR (qPCR) was used to validate key findings. Statistical analyses were conducted to assess differences in microbial diversity, relative abundance, and metabolite levels between the groups.

Results

Significant differences in alpha diversity at the species level were observed between the R and NR groups. Bacteroides acidifaciens was enriched in the NR group, while Propionibacterium acnes and Clostridium magna were more abundant in the R group. Machine learning identified Acidosoma, Propionibacterium acnes, and Clostridium magna as key predictors of radiotherapy response. Metabolomic profiling revealed elevated acetate levels in the NR group, implicating its role in tumor growth and immune evasion. Validation via qPCR confirmed the differential abundance of these microbial taxa in both tumor tissue and fecal samples.

Discussion

Our findings highlight the interplay between microbiota and metabolite profiles in influencing radiotherapy outcomes in NPC. These results suggest that targeting the microbiota-metabolite axis may enhance radiotherapy efficacy in NPC.

Dual graph-embedded fusion network for predicting potential microbe-disease associations with sequence learning

Recent studies indicate that microorganisms are crucial for maintaining human health. Dysbiosis, or an imbalance in these microbial communities, is strongly linked to a variety of human diseases. Therefore, understanding the impact of microbes on disease is essential. The DuGEL model leverages the strengths of graph convolutional neural network (GCN) and graph attention network (GAT), ensuring that both local and global relationships within the microbe-disease association network are captured. The integration of the Long Short-Term Memory Network (LSTM) further enhances the model's ability to understand sequential dependencies in the feature representations. This comprehensive approach allows DuGEL to achieve a high level of accuracy in predicting potential microbe-disease associations, making it a valuable tool for biomedical research and the discovery of new therapeutic targets. By combining advanced graph-based and sequence-based learning techniques, DuGEL addresses the limitations of existing methods and provides a robust framework for the prediction of microbe-disease associations. To evaluate the performance of DuGEL, we conducted comprehensive comparative experiments and case studies based on two databases, HMDAD, and Disbiome to demonstrate that DuGEL can effectively predict potential microbe-disease associations.

Peeling off the layers from microbial dark matter (MDM): recent advances, future challenges, and opportunities

Microbes represent the most common organisms on Earth; however, less than 2% of microbial species in the environment can undergo cultivation for study under laboratory conditions, and the rest of the enigmatic, microbial world remains mysterious, constituting a kind of "microbial dark matter" (MDM). In the last two decades, remarkable progress has been made in culture-dependent and culture-independent techniques. More recently, studies of MDM have relied on culture-independent techniques to recover genetic material through either unicellular genomics or shotgun metagenomics to construct single-amplified genomes (SAGs) and metagenome-assembled genomes (MAGs), respectively, which provide information about evolution and metabolism. Despite the remarkable progress made in the past decades, the functional diversity of MDM still remains uncharacterized. This review comprehensively summarizes the recently developed culture-dependent and culture-independent techniques for characterizing MDM, discussing major challenges, opportunities, and potential applications. These activities contribute to expanding our knowledge of the microbial world and have implications for various fields including Biotechnology, Bioprospecting, Functional genomics, Medicine, Evolutionary and Planetary biology. Overall, this review aims to peel off the layers from MDM, shed light on recent advancements, identify future challenges, and illuminate the exciting opportunities that lie ahead in unraveling the secrets of this intriguing microbial realm.

Deep learning in microbiome analysis: a comprehensive review of neural network models

Microbiome research, the study of microbial communities in diverse environments, has seen significant advances due to the integration of deep learning (DL) methods. These computational techniques have become essential for addressing the inherent complexity and high-dimensionality of microbiome data, which consist of different types of omics datasets. Deep learning algorithms have shown remarkable capabilities in pattern recognition, feature extraction, and predictive modeling, enabling researchers to uncover hidden relationships within microbial ecosystems. By automating the detection of functional genes, microbial interactions, and host-microbiome dynamics, DL methods offer unprecedented precision in understanding microbiome composition and its impact on health, disease, and the environment. However, despite their potential, deep learning approaches face significant challenges in microbiome research. Additionally, the biological variability in microbiome datasets requires tailored approaches to ensure robust and generalizable outcomes. As microbiome research continues to generate vast and complex datasets, addressing these challenges will be crucial for advancing microbiological insights and translating them into practical applications with DL. This review provides an overview of different deep learning models in microbiome research, discussing their strengths, practical uses, and implications for future studies. We examine how these models are being applied to solve key problems and highlight potential pathways to overcome current limitations, emphasizing the transformative impact DL could have on the field moving forward.

Effects of data transformation and model selection on feature importance in microbiome classification data

BACKGROUND

Accurate classification of host phenotypes from microbiome data is crucial for advancing microbiome-based therapies, with machine learning offering effective solutions. However, the complexity of the gut microbiome, data sparsity, compositionality, and population-specificity present significant challenges. Microbiome data transformations can alleviate some of the aforementioned challenges, but their usage in machine learning tasks has largely been unexplored.

RESULTS

Our analysis of over 8500 samples from 24 shotgun metagenomic datasets showed that it is possible to classify healthy and diseased individuals using microbiome data with minimal dependence on the choice of algorithm or transformation. Presence-absence transformations performed comparably to abundance-based transformations, and only a small subset of predictors is necessary for accurate classification. However, while different transformations resulted in comparable classification performance, the most important features varied significantly, which highlights the need to reevaluate machine learning-based biomarker detection.

CONCLUSIONS

Microbiome data transformations can significantly influence feature selection but have a limited effect on classification accuracy. Our findings suggest that while classification is robust across different transformations, the variation in feature selection necessitates caution when using machine learning for biomarker identification. This research provides valuable insights for applying machine learning to microbiome data and identifies important directions for future work.

Bacterial profile-based body fluid identification using a machine learning approach

BACKGROUND

Identifying the origins of biological traces is critical for the reconstruction of crime scenes in forensic investigations. Traditional methods for body fluid identification rely on chemical, enzymatic, immunological, and spectroscopic techniques, which can be sample-consuming and depend on simple color-change reactions. However, these methods have limitations when residual samples are insufficient after DNA extraction.

OBJECTIVE

This study aimed to develop a method for body fluid identification by leveraging bacterial DNA profiling to overcome the limitations of the conventional approaches.

METHODS

Bacterial profiles were determined by sequencing the hypervariable region of the 16 S rRNA gene, using DNA metabarcoding of evidence collected from criminal cases. Amplicon sequence variants (ASVs) were analyzed to identify significant microbial patterns in different body fluid samples.

RESULTS

The bacterial profile-based method demonstrated high discriminatory power with a machine learning model trained using the naïve Bayes algorithm, achieving an accuracy of over 98% in classifying samples into one of four body fluid types: blood, saliva, vaginal secretion, and mixture traces of vaginal secretions and semen.

CONCLUSION

Bacterial profiling enhances the accuracy and robustness of body fluid identification in forensic analysis, providing a valuable alternative to traditional methods by utilizing DNA and microbial community data despite the uncontrollable conditions. This approach offers significant improvements in the classification accuracy and practical applicability in forensic investigations.

Using GWAS and Machine Learning to Identify and Predict Genetic Variants Associated with Foodborne Bacteria Phenotypic Traits

One of the main challenges in food microbiology is to prevent the risk of outbreaks by avoiding the distribution of food contaminated by bacteria. This requires constant monitoring of the circulating strains throughout the food production chain. Bacterial genomes contain signatures of natural evolution and adaptive markers that can be exploited to better understand the behavior of pathogen in the food industry. The monitoring of foodborne strains can therefore be facilitated by the use of these genomic markers capable of rapidly providing essential information on isolated strains, such as the source of contamination, risk of illness, potential for biofilm formation, and tolerance or resistance to biocides. The increasing availability of large genome datasets is enhancing the understanding of the genetic basis of complex traits such as host adaptation, virulence, and persistence. Genome-wide association studies have shown very promising results in the discovery of genomic markers that can be integrated into rapid detection tools. In addition, machine learning has successfully predicted phenotypes and classified important traits. Genome-wide association and machine learning tools have therefore the potential to support decision-making circuits intending at reducing the burden of foodborne diseases. The aim of this chapter review is to provide knowledge on the use of these two methods in food microbiology and to recommend their use in the field.

Gut microbiome-gut brain axis-depression: interconnection

OBJECTIVES

The relationship between the gut microbiome and mental health, particularly depression, has gained significant attention. This review explores the connection between microbial metabolites, dysbiosis, and depression. The gut microbiome, comprising diverse microorganisms, maintains physiological balance and influences health through the gut-brain axis, a communication pathway between the gut and the central nervous system.

METHODS

Dysbiosis, an imbalance in the gut microbiome, disrupts this axis and worsens depressive symptoms. Factors like diet, antibiotics, and lifestyle can cause this imbalance, leading to changes in microbial composition, metabolism, and immune responses. This imbalance can induce inflammation, disrupt neurotransmitter regulation, and affect hormonal and epigenetic processes, all linked to depression.

RESULTS

Microbial metabolites, such as short-chain fatty acids and neurotransmitters, are key to gut-brain communication, influencing immune regulation and mood. The altered production of these metabolites is associated with depression. While progress has been made in understanding the gut-brain axis, more research is needed to clarify causative relationships and develop new treatments. The emerging field of psychobiotics and microbiome-targeted therapies shows promise for innovative depression treatments by harnessing the gut microbiome's potential.

CONCLUSIONS

Epigenetic mechanisms, including DNA methylation and histone modifications, are crucial in how the gut microbiota impacts mental health. Understanding these mechanisms offers new prospects for preventing and treating depression through the gut-brain axis.

Multitask knowledge-primed neural network for predicting missing metadata and host phenotype based on human microbiome

Motivation

Microbial signatures in the human microbiome are closely associated with various human diseases, driving the development of machine learning models for microbiome-based disease prediction. Despite progress, challenges remain in enhancing prediction accuracy, generalizability, and interpretability. Confounding factors, such as host's gender, age, and body mass index, significantly influence the human microbiome, complicating microbiome-based predictions.

Results

To address these challenges, we developed MicroKPNN-MT, a unified model for predicting human phenotype based on microbiome data, as well as additional metadata like age and gender. This model builds on our earlier MicroKPNN framework, which incorporates prior knowledge of microbial species into neural networks to enhance prediction accuracy and interpretability. In MicroKPNN-MT, metadata, when available, serves as additional input features for prediction. Otherwise, the model predicts metadata from microbiome data using additional decoders. We applied MicroKPNN-MT to microbiome data collected in mBodyMap, covering healthy individuals and 25 different diseases, and demonstrated its potential as a predictive tool for multiple diseases, which at the same time provided predictions for the missing metadata. Our results showed that incorporating real or predicted metadata helped improve the accuracy of disease predictions, and more importantly, helped improve the generalizability of the predictive models.

Availability and implementation

https://github.com/mgtools/MicroKPNN-MT.

Exploring and exploiting the rice phytobiome to tackle climate change challenges

The future of agriculture is uncertain under the current climate change scenario. Climate change directly and indirectly affects the biotic and abiotic elements that control agroecosystems, jeopardizing the safety of the world's food supply. A new area that focuses on characterizing the phytobiome is emerging. The phytobiome comprises plants and their immediate surroundings, involving numerous interdependent microscopic and macroscopic organisms that affect the health and productivity of plants. Phytobiome studies primarily focus on the microbial communities associated with plants, which are referred to as the plant microbiome. The development of high-throughput sequencing technologies over the past 10 years has dramatically advanced our understanding of the structure, functionality, and dynamics of the phytobiome; however, comprehensive methods for using this knowledge are lacking, particularly for major crops such as rice. Considering the impact of rice production on world food security, gaining fresh perspectives on the interdependent and interrelated components of the rice phytobiome could enhance rice production and crop health, sustain rice ecosystem function, and combat the effects of climate change. Our review re-conceptualizes the complex dynamics of the microscopic and macroscopic components in the rice phytobiome as influenced by human interventions and changing environmental conditions driven by climate change. We also discuss interdisciplinary and systematic approaches to decipher and reprogram the sophisticated interactions in the rice phytobiome using novel strategies and cutting-edge technology. Merging the gigantic datasets and complex information on the rice phytobiome and their application in the context of regenerative agriculture could lead to sustainable rice farming practices that are resilient to the impacts of climate change.

Explainable machine learning model for identifying key gut microbes and metabolites biomarkers associated with myasthenia gravis

Diagnostic markers for myasthenia gravis (MG) are limited; thus, innovative approaches are required for supportive diagnosis and personalized care. Gut microbes are associated with MG pathogenesis; however, few studies have adopted machine learning (ML) to identify the associations among MG, gut microbiota, and metabolites. In this study, we developed an explainable ML model to predict biomarkers for MG diagnosis. We enrolled 19 MG patients and 10 non-MG individuals. Stool samples were collected and microbiome assessment was performed using 16S rRNA sequencing. Untargeted metabolic profiling was conducted to identify fecal amplicon significant variants (ASVs) and metabolites. We developed an explainable ML model in which the top ASVs and metabolites are combined to identify the best predictive performance. This model uses the SHapley Additive exPlanations method to generate both global and personalized explanations. Fecal microbe-metabolite composition differed significantly between groups. The key bacterial families were Lachnospiraceae and Ruminococcaceae, and the top three features were Lachnospiraceae, inosine, and methylhistidine. An ML model trained with the top 1 % ASVs and top 15 % metabolites combined outperformed all other models. Personalized explanations revealed different patterns of microbe-metabolite contributions in patients with MG. The integration of the microbiota-metabolite features and the development of an explainable ML framework can accurately identify MG and provide personalized explanations, revealing the associations between gut microbiota, metabolites, and MG. An online calculator employing this algorithm was developed that provides a streamlined interface for MG diagnosis screening and conducting personalized evaluations.

Wise Roles and Future Visionary Endeavors of Current Emperor: Advancing Dynamic Methods for Longitudinal Microbiome Meta-Omics Data in Personalized and Precision Medicine

Understanding the etiological complexity of diseases requires identifying biomarkers longitudinally associated with specific phenotypes. Advanced sequencing tools generate dynamic microbiome data, providing insights into microbial community functions and their impact on health. This review aims to explore the current roles and future visionary endeavors of dynamic methods for integrating longitudinal microbiome multi-omics data in personalized and precision medicine. This work seeks to synthesize existing research, propose best practices, and highlight innovative techniques. The development and application of advanced dynamic methods, including the unified analytical frameworks and deep learning tools in artificial intelligence, are critically examined. Aggregating data on microbes, metabolites, genes, and other entities offers profound insights into the interactions among microorganisms, host physiology, and external stimuli. Despite progress, the absence of gold standards for validating analytical protocols and data resources of various longitudinal multi-omics studies remains a significant challenge. The interdependence of workflow steps critically affects overall outcomes. This work provides a comprehensive roadmap for best practices, addressing current challenges with advanced dynamic methods. The review underscores the biological effects of clinical, experimental, and analytical protocol settings on outcomes. Establishing consensus on dynamic microbiome inter-studies and advancing reliable analytical protocols are pivotal for the future of personalized and precision medicine.

Prediction of Crohn's disease based on deep feature recognition

BACKGROUND

Crohn's disease is a complex genetic disease that involves chronic gastrointestinal inflammation and results from a complex set of genetic, environmental, and immunological factors. By analyzing data from the human microbiome, genetic information can be used to predict Crohn's disease. Recent advances in deep learning have demonstrated its effectiveness in feature extraction and the use of deep learning to decode genetic information for disease prediction.

METHODS

In this paper, we present a deep learning-based model that utilizes a sequential convolutional attention network (SCAN) for feature extraction, incorporates adaptive additive interval losses to enhance these features, and employs support vector machines (SVM) for classification. To address the challenge of unbalanced Crohn's disease samples, we propose a random noise one-hot encoding data augmentation method.

RESULTS

Data augmentation with random noise accelerates training convergence, while SCAN-SVM effectively extracts features with adaptive additive interval loss enhancing differentiation. Our approach outperforms benchmark methods, achieving an average accuracy of 0.80 and a kappa value of 0.76, and we validate the effectiveness of feature enhancement.

CONCLUSIONS

In summary, we use deep feature recognition to effectively analyze the potential information in genes, which has a good application potential for gene analysis and prediction of Crohn's disease.

Optimizing microbiome reference databases with PacBio full-length 16S rRNA sequencing for enhanced taxonomic classification and biomarker discovery

Background

The study of the human microbiome is crucial for understanding disease mechanisms, identifying biomarkers, and guiding preventive measures. Advances in sequencing platforms, particularly 16S rRNA sequencing, have revolutionized microbiome research. Despite the benefits, large microbiome reference databases (DBs) pose challenges, including computational demands and potential inaccuracies. This study aimed to determine if full-length 16S rRNA sequencing data produced by PacBio could be used to optimize reference DBs and be applied to Illumina V3-V4 targeted sequencing data for microbial study.

Methods

Oral and gut microbiome data (PRJNA1049979) were retrieved from NCBI. DADA2 was applied to full-length 16S rRNA PacBio data to obtain amplicon sequencing variants (ASVs). The RDP reference DB was used to assign the ASVs, which were then used as a reference DB to train the classifier. QIIME2 was used for V3-V4 targeted Illumina data analysis. BLAST was used to analyze alignment statistics. Linear discriminant analysis Effect Size (LEfSe) was employed for discriminant analysis.

Results

ASVs produced by PacBio showed coverage of the oral microbiome similar to the Human Oral Microbiome Database. A phylogenetic tree was trimmed at various thresholds to obtain an optimized reference DB. This established method was then applied to gut microbiome data, and the optimized gut microbiome reference DB provided improved taxa classification and biomarker discovery efficiency.

Conclusion

Full-length 16S rRNA sequencing data produced by PacBio can be used to construct a microbiome reference DB. Utilizing an optimized reference DB can increase the accuracy of microbiome classification and enhance biomarker discovery.

A deep learning feature importance test framework for integrating informative high-dimensional biomarkers to improve disease outcome prediction

Many human diseases result from a complex interplay of behavioral, clinical, and molecular factors. Integrating low-dimensional behavioral and clinical features with high-dimensional molecular profiles can significantly improve disease outcome prediction and diagnosis. However, while some biomarkers are crucial, many lack informative value. To enhance prediction accuracy and understand disease mechanisms, it is essential to integrate relevant features and identify key biomarkers, separating meaningful data from noise and modeling complex associations. To address these challenges, we introduce the High-dimensional Feature Importance Test (HdFIT) framework for machine learning models. HdFIT includes a feature screening step for dimension reduction and leverages machine learning to model complex associations between biomarkers and disease outcomes. It robustly evaluates each feature's impact. Extensive Monte Carlo experiments and a real microbiome study demonstrate HdFIT's efficacy, especially when integrated with advanced models like deep neural networks. Our framework shows significant improvements in identifying crucial features and enhancing prediction accuracy, even in high-dimensional settings.

Techniques for learning and transferring knowledge for microbiome-based classification and prediction: review and assessment

The volume of microbiome data is growing at an exponential rate, and the current methodologies for big data mining are encountering substantial obstacles. Effectively managing and extracting valuable insights from these vast microbiome datasets has emerged as a significant challenge in the field of contemporary microbiome research. This comprehensive review delves into the utilization of foundation models and transfer learning techniques within the context of microbiome-based classification and prediction tasks, advocating for a transition away from traditional task-specific or scenario-specific models towards more adaptable, continuous learning models. The article underscores the practicality and benefits of initially constructing a robust foundation model, which can then be fine-tuned using transfer learning to tackle specific context tasks. In real-world scenarios, the application of transfer learning empowers models to leverage disease-related data from one geographical area and enhance diagnostic precision in different regions. This transition from relying on "good models" to embracing "adaptive models" resonates with the philosophy of "teaching a man to fish" thereby paving the way for advancements in personalized medicine and accurate diagnosis. Empirical research suggests that the integration of foundation models with transfer learning methodologies substantially boosts the performance of models when dealing with large-scale and diverse microbiome datasets, effectively mitigating the challenges posed by data heterogeneity.

Gut Microbial Taxonomy and Its Role as a Biomarker in Aortic Diseases: A Systematic Review and Future Perspectives

Background/Objectives: Evidence of the association between the gut microbiome and cardiovascular diseases has accumulated. An imbalance or dysbiosis of this system has been shown to play a role in the pathogenesis of cardiovascular events, including aortic diseases. We aimed to elucidate the findings of the gut microbial taxonomy associated with aortic diseases and their subtypes. Furthermore, we sought to investigate whether gut microbiome dysbiosis can be used as a biomarker for aortic disease detection and to identify which species can be disease-specific. Methods: A systematic search was conducted using the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines for original research papers on gut microbiome composition in patients with aortic disease, using patients without aortic disease as the control (i.e., healthy controls). The databases PubMed, Scopus, Cochrane, and Web of Science were used by employing the medical subject headings (MeSH) terms "aortic diseases", "microbiome"," microbiota", and "taxa" before August 2024. We extracted the study characteristics, study population, and gut microbiome in aortic disease, including microbiota taxa diversity and abundance, regardless of taxa level. The National Institutes of Health (NIH) Quality Assessment Tool was used to assess study quality. Data were synthesized narratively to address the heterogeneity of the studies. Results: In this review, twelve studies that have identified gut microbial species and their potential impact on aortic disease pathogenesis were included. The studies showed the phyla dominance of Bacillota, Pseudomonadota, Actinomycetota, Bacteroidota, and Euryarchaeota in aortic disease patients. We also included the taxa sequencing methods and those used to extract the microorganisms. Aortic diseases were categorized into Takayasu's arteritis, giant cell arteritis, aortic aneurysm, and aortic dissection. Aortic disease patients had a higher rate of dysbiosis when compared to the healthy control groups, with significantly different microbiome composition. Conclusions: Patients with aortic disease exhibit a distinct difference between their gut microbiota composition and that of the healthy controls, which suggests a potential biomarker role of gut dysbiosis. Further exploration of the microbiome and its metagenome interface can help identify its role in aortic disease pathogenesis in depth, generating future therapeutic options. However, a unified methodology is required to identify potential microbial biomarkers in cardiovascular and cardiometabolic diseases.

Utilizing Artificial Intelligence for Microbiome Decision-Making: Autism Spectrum Disorder in Children from Bosnia and Herzegovina

BACKGROUND/OBJECTIVES

The study of microbiome composition shows positive indications for application in the diagnosis and treatment of many conditions and diseases. One such condition is autism spectrum disorder (ASD). We aimed to analyze gut microbiome samples from children in Bosnia and Herzegovina to identify microbial differences between neurotypical children and those with ASD. Additionally, we developed machine learning classifiers to differentiate between the two groups using microbial abundance and predicted functional pathways.

METHODS

A total of 60 gut microbiome samples (16S rRNA sequences) were analyzed, with 44 from children with ASD and 16 from neurotypical children. Four machine learning algorithms (Random Forest, Support Vector Classification, Gradient Boosting, and Extremely Randomized Tree Classifier) were applied to create eight classification models based on bacterial abundance at the genus level and KEGG pathways. Model accuracy was evaluated, and an external dataset was introduced to test model generalizability.

RESULTS

The highest classification accuracy (80%) was achieved with Random Forest and Extremely Randomized Tree Classifier using genus-level taxa. The Random Forest model also performed well (78%) with KEGG pathways. When tested on an independent dataset, the model maintained high accuracy (79%), confirming its generalizability.

CONCLUSIONS

This study identified significant microbial differences between neurotypical children and children with ASD. Machine learning classifiers, particularly Random Forest and Extremely Randomized Tree Classifier, achieved strong accuracy. Validation with external data demonstrated that the models could generalize across different datasets, highlighting their potential use.

CCPred: Global and population-specific colorectal cancer prediction and metagenomic biomarker identification at different molecular levels using machine learning techniques

Colorectal cancer (CRC) ranks as the third most common cancer globally and the second leading cause of cancer-related deaths. Recent research highlights the pivotal role of the gut microbiota in CRC development and progression. Understanding the complex interplay between disease development and metagenomic data is essential for CRC diagnosis and treatment. Current computational models employ machine learning to identify metagenomic biomarkers associated with CRC, yet there is a need to improve their accuracy through a holistic biological knowledge perspective. This study aims to evaluate CRC-associated metagenomic data at species, enzymes, and pathway levels via conducting global and population-specific analyses. These analyses utilize relative abundance values from human gut microbiome sequencing data and robust classification models are built for disease prediction and biomarker identification. For global CRC prediction and biomarker identification, the features that are identified by SelectKBest (SKB), Information Gain (IG), and Extreme Gradient Boosting (XGBoost) methods are combined. Population-based analysis includes within-population, leave-one-dataset-out (LODO) and cross-population approaches. Four classification algorithms are employed for CRC classification. Random Forest achieved an AUC of 0.83 for species data, 0.78 for enzyme data and 0.76 for pathway data globally. On the global scale, potential taxonomic biomarkers include ruthenibacterium lactatiformanas; enzyme biomarkers include RNA 2' 3' cyclic 3' phosphodiesterase; and pathway biomarkers include pyruvate fermentation to acetone pathway. This study underscores the potential of machine learning models trained on metagenomic data for improved disease prediction and biomarker discovery. The proposed model and associated files are available at https://github.com/TemizMus/CCPRED.

Addressing the surge of infections by multidrug-resistant Enterobacterales in hematopoietic cell transplantation

Patients undergoing hematopoietic cell transplantation (HCT) have an increased risk of developing severe infections. In recent years, bloodstream infections caused by Gram-negative bacteria have been increasingly reported among HCT recipients, and many of these infections are caused by bacterial strains of the Enterobacterales order. Among these pathogens, particularly concerning are the multidrug-resistant Enterobacterales (MDRE), such as Extended Spectrum β-lactamase-producing Enterobacterales and Carbapenem-resistant Enterobacterales, since infections caused by these pathogens are difficult to treat due to the limited antimicrobial options and are associated with worse transplant outcomes. We summarized the evidence from studies published in PubMed and Scopus on the burden of MDRE infections in HCT recipients, and strategies for the management and prevention of these infections, including strict adherence to recommended infection control practices and multidisciplinary antimicrobial stewardship, the use of probiotics, and fecal microbiota transplantation, are also discussed.

Revolution in malaria detection: unveiling current breakthroughs and tomorrow's possibilities in biomarker innovation

The ongoing battle against malaria has seen significant advancements in diagnostic methodologies, particularly through the discovery and application of novel biomarkers. Traditional diagnostic techniques, such as microscopy and rapid diagnostic tests, have their limitations in terms of sensitivity, specificity, and the ability to detect low-level infections. Recent breakthroughs in biomarker research promise to overcome these challenges, providing more accurate, rapid, and non-invasive detection methods. These advancements are critical in enhancing early detection, guiding effective treatment, and ultimately reducing the global malaria burden. Innovative approaches in biomarker detection are leveraging cutting-edge technologies like next-generation sequencing, proteomics, and metabolomics. These techniques have led to the identification of new biomarkers that can be detected in blood, saliva, or urine, offering less invasive and more scalable options for widespread screening. For instance, the discovery of specific volatile organic compounds in the breath of infected individuals presents a revolutionary non-invasive diagnostic tool. Additionally, the integration of machine learning algorithms with biomarker data is enhancing the precision and predictive power of malaria diagnostics, making it possible to distinguish between different stages of infection and identify drug-resistant strains. Looking ahead, the future of malaria detection lies in the continued exploration of multi-biomarker panels and the development of portable, point-of-care diagnostic devices. The incorporation of smartphone-based technologies and wearable biosensors promises to bring real-time monitoring and remote diagnostics to even the most resource-limited settings.

Robust prediction of colorectal cancer via gut microbiome 16S rRNA sequencing data

Introduction. The study addresses the challenge of utilizing human gut microbiome data for the early detection of colorectal cancer (CRC). The research emphasizes the potential of using machine learning techniques to analyze complex microbiome datasets, providing a non-invasive approach to identifying CRC-related microbial markers.Hypothesis/Gap Statement. The primary hypothesis is that a robust machine learning-based analysis of 16S rRNA microbiome data can identify specific microbial features that serve as effective biomarkers for CRC detection, overcoming the limitations of classical statistical models in high-dimensional settings.Aim. The primary objective of this study is to explore and validate the potential of the human microbiome, specifically in the colon, as a valuable source of biomarkers for colorectal cancer (CRC) detection and progression. The focus is on developing a classifier that effectively predicts the presence of CRC and normal samples based on the analysis of three previously published faecal 16S rRNA sequencing datasets.Methodology. To achieve the aim, various machine learning techniques are employed, including random forest (RF), recursive feature elimination (RFE) and a robust correlation-based technique known as the fuzzy forest (FF). The study utilizes these methods to analyse the three datasets, comparing their performance in predicting CRC and normal samples. The emphasis is on identifying the most relevant microbial features (taxa) associated with CRC development via partial dependence plots, i.e. a machine learning tool focused on explainability, visualizing how a feature influences the predicted outcome.Results. The analysis of the three faecal 16S rRNA sequencing datasets reveals the consistent and superior predictive performance of the FF compared to the RF and RFE. Notably, FF proves effective in addressing the correlation problem when assessing the importance of microbial taxa in explaining the development of CRC. The results highlight the potential of the human microbiome as a non-invasive means to detect CRC and underscore the significance of employing FF for improved predictive accuracy.Conclusion. In conclusion, this study underscores the limitations of classical statistical techniques in handling high-dimensional information such as human microbiome data. The research demonstrates the potential of the human microbiome, specifically in the colon, as a valuable source of biomarkers for CRC detection. Applying machine learning techniques, particularly the FF, is a promising approach for building a classifier to predict CRC and normal samples. The findings advocate for integrating FF to overcome the challenges associated with correlation when identifying crucial microbial features linked to CRC development.

Concepts and criteria defining emerging microbiome applications

In recent years, microbiomes and their potential applications for human, animal or plant health, food production and environmental management came into the spotlight of major national and international policies and strategies. This has been accompanied by substantial R&D investments in both public and private sectors, with an increasing number of products entering the market. Despite widespread agreement on the potential of microbiomes and their uses across disciplines, stakeholders and countries, there is no consensus on what defines a microbiome application. This often results in non-comprehensive communication or insufficient documentation making commercialisation and acceptance of the novel products challenging. To showcase the complexity of this issue we discuss two selected, well-established applications and propose criteria defining a microbiome application and their conditions of use for clear communication, facilitating suitable regulatory frameworks and building trust among stakeholders.

Machine learning for the advancement of genome-scale metabolic modeling

Constraint-based modeling (CBM) has evolved as the core systems biology tool to map the interrelations between genotype, phenotype, and external environment. The recent advancement of high-throughput experimental approaches and multi-omics strategies has generated a plethora of new and precise information from wide-ranging biological domains. On the other hand, the continuously growing field of machine learning (ML) and its specialized branch of deep learning (DL) provide essential computational architectures for decoding complex and heterogeneous biological data. In recent years, both multi-omics and ML have assisted in the escalation of CBM. Condition-specific omics data, such as transcriptomics and proteomics, helped contextualize the model prediction while analyzing a particular phenotypic signature. At the same time, the advanced ML tools have eased the model reconstruction and analysis to increase the accuracy and prediction power. However, the development of these multi-disciplinary methodological frameworks mainly occurs independently, which limits the concatenation of biological knowledge from different domains. Hence, we have reviewed the potential of integrating multi-disciplinary tools and strategies from various fields, such as synthetic biology, CBM, omics, and ML, to explore the biochemical phenomenon beyond the conventional biological dogma. How the integrative knowledge of these intersected domains has improved bioengineering and biomedical applications has also been highlighted. We categorically explained the conventional genome-scale metabolic model (GEM) reconstruction tools and their improvement strategies through ML paradigms. Further, the crucial role of ML and DL in omics data restructuring for GEM development has also been briefly discussed. Finally, the case-study-based assessment of the state-of-the-art method for improving biomedical and metabolic engineering strategies has been elaborated. Therefore, this review demonstrates how integrating experimental and in silico strategies can help map the ever-expanding knowledge of biological systems driven by condition-specific cellular information. This multiview approach will elevate the application of ML-based CBM in the biomedical and bioengineering fields for the betterment of society and the environment.

A cross-cohort analysis of dental plaque microbiome in early childhood caries

Early childhood caries (ECC) is a multifactorial disease with a microbiome playing a significant role in caries progression. Understanding changes at the microbiome level in ECC is required to develop diagnostic and preventive strategies. In our study, we combined data from small independent cohorts to compare microbiome composition using a unified pipeline and applied a batch correction to avoid the pitfalls of batch effects. Our meta-analysis identified common biomarker species between different studies. We identified the best machine learning method for the classification of ECC versus caries-free samples and compared the performance of this method using a leave-one-dataset-out approach. Our random forest model was found to be generalizable when used in combination with other studies. While our results highlight the potential microbial species involved in ECC and disease classification, we also mentioned the limitations that can serve as a guide for future researchers to design and use appropriate tools for such analyses.

Intra-hospital microbiome variability is driven by accessibility and clinical activities

The hospital environmental microbiome, which can affect patients' and healthcare workers' health, is highly variable and the drivers of this variability are not well understood. In this study, we collected 37 surface samples from the neonatal intensive care unit (NICU) in an inpatient hospital before and after the operation began. Additionally, healthcare workers collected 160 surface samples from five additional areas of the hospital. All samples were analyzed using 16S rRNA gene amplicon sequencing, and the samples collected by healthcare workers were cultured. The NICU samples exhibited similar alpha and beta diversities before and after opening, which indicated that the microbiome there was stable over time. Conversely, the diversities of samples taken after opening varied widely by area. Principal coordinate analysis (PCoA) showed the samples clustered into two distinct groups: high alpha diversity [the pediatric intensive care unit (PICU), pathology lab, and microbiology lab] and low alpha diversity [the NICU, pediatric surgery ward, and infection prevention and control (IPAC) office]. Least absolute shrinkage and selection operator (LASSO) classification models identified 156 informative amplicon sequence variants (ASVs) for predicting the sample's area of origin. The testing accuracy ranged from 86.37% to 100%, which outperformed linear and radial support vector machine (SVM) and random forest models. ASVs of genera that contain emerging pathogens were identified in these models. Culture experiments had identified viable species among the samples, including potential antibiotic-resistant bacteria. Though area type differences were not noted in the culture data, the prevalences and relative abundances of genera detected positively correlated with 16S sequencing data. This study brings to light the microbial community temporal and spatial variation within the hospital and the importance of pathogenic and commensal bacteria to understanding dispersal patterns for infection control.

IMPORTANCE

We sampled surface samples from a newly built inpatient hospital in multiple areas, including areas accessed by only healthcare workers. Our analysis of the neonatal intensive care unit (NICU) showed that the microbiome was stable before and after the operation began, possibly due to access restrictions. Of the high-touch samples taken after opening, areas with high diversity had more potential external seeds (long-term patients and clinical samples), and areas with low diversity and had fewer (short-term or newborn patients). Classification models performed at high accuracy and identified biomarkers that could be used for more targeted surveillance and infection control. Though culturing data yielded viability and antibiotic-resistance information, it disproportionately detected the presence of genera relative to 16S data. This difference reinforces the utility of 16S sequencing in profiling hospital microbiomes. By examining the microbiome over time and in multiple areas, we identified potential drivers of the microbial variation within a hospital.

Predicting potential microbe-disease associations based on dual branch graph convolutional network

Studying the association between microbes and diseases not only aids in the prevention and diagnosis of diseases, but also provides crucial theoretical support for new drug development and personalized treatment. Due to the time-consuming and costly nature of laboratory-based biological tests to confirm the relationship between microbes and diseases, there is an urgent need for innovative computational frameworks to anticipate new associations between microbes and diseases. Here, we propose a novel computational approach based on a dual branch graph convolutional network (GCN) module, abbreviated as DBGCNMDA, for identifying microbe-disease associations. First, DBGCNMDA calculates the similarity matrix of diseases and microbes by integrating functional similarity and Gaussian association spectrum kernel (GAPK) similarity. Then, semantic information from different biological networks is extracted by two GCN modules from different perspectives. Finally, the scores of microbe-disease associations are predicted based on the extracted features. The main innovation of this method lies in the use of two types of information for microbe/disease similarity assessment. Additionally, we extend the disease nodes to address the issue of insufficient features due to low data dimensionality. We optimize the connectivity between the homogeneous entities using random walk with restart (RWR), and then use the optimized similarity matrix as the initial feature matrix. In terms of network understanding, we design a dual branch GCN module, namely GlobalGCN and LocalGCN, to fine-tune node representations by introducing side information, including homologous neighbour nodes. We evaluate the accuracy of the DBGCNMDA model using five-fold cross-validation (5-fold-CV) technique. The results show that the area under the receiver operating characteristic curve (AUC) and area under the precision versus recall curve (AUPR) of the DBGCNMDA model in the 5-fold-CV are 0.9559 and 0.9630, respectively. The results from the case studies using published experimental data confirm a significant number of predicted associations, indicating that DBGCNMDA is an effective tool for predicting potential microbe-disease associations.

Distinct prophage gene profiles of Staphylococcus aureus strains from atopic dermatitis patients and healthy individuals

Staphylococcus aureus strains exhibit varying associations with atopic dermatitis (AD), but the genetic determinants underpinning the pathogenicity are yet to be fully characterized. To reveal the genetic differences between S. aureus strains from AD patients and healthy individuals (HE), we developed and employed a random forest classifier to identify potential marker genes responsible for their phenotypic variations. The classifier was able to effectively distinguish strains from AD and HE. We also uncovered strong links between certain marker genes and phage functionalities, with phage holin emerging as the most pivotal differentiating factor. Further examination of S. aureus gene content highlighted the genetic diversity and functional implications of prophages in driving differentiation between strains from AD and HE. The HE group exhibited greater gene content diversity, largely influenced by their prophages. While strains from both AD and HE universally housed prophages, those in the HE group were distinctively higher at the strain level. Moreover, although prophages in the HE group exhibited variously higher enrichment of differential functions, the AD group displayed a notable enrichment of virulence factors within their prophages, underscoring the important contribution of prophages to the pathogenesis of AD-associated strains. Overall, prophages significantly shape the genetic and functional profiles of S. aureus strains, shedding light on their pathogenic potential and elucidating the mechanisms behind the phenotypic variations in AD and HE environments.

IMPORTANCE

Through a nuanced exploration of Staphylococcus aureus strains obtained from atopic dermatitis (AD) patients and healthy controls (HE), our research unveils pivotal genetic determinants influencing their pathogenic associations. Utilizing a random forest classifier, we illuminate distinct marker genes, with phage holin emerging as a critical differential factor, revealing the profound impact of prophages on genetic and pathogenic profiles. HE strains exhibited a diverse gene content, notably shaped by unique, heightened prophages. Conversely, AD strains emphasized a pronounced enrichment of virulence factors within prophages, signifying their key role in AD pathogenesis. This work crucially highlights prophages as central architects of the genetic and functional attributes of S. aureus strains, providing vital insights into pathogenic mechanisms and phenotypic variations, thereby paving the way for targeted AD therapeutic approaches and management strategies by demystifying specific genetic and pathogenic mechanisms.

Machine learning algorithms to predict healthcare-seeking behaviors of mothers for acute respiratory infections and their determinants among children under five in sub-Saharan Africa

Background

Acute respiratory infections (ARIs) are the leading cause of death in children under the age of 5 globally. Maternal healthcare-seeking behavior may help minimize mortality associated with ARIs since they make decisions about the kind and frequency of healthcare services for their children. Therefore, this study aimed to predict the absence of maternal healthcare-seeking behavior and identify its associated factors among children under the age 5 in sub-Saharan Africa (SSA) using machine learning models.

Methods

The sub-Saharan African countries' demographic health survey was the source of the dataset. We used a weighted sample of 16,832 under-five children in this study. The data were processed using Python (version 3.9), and machine learning models such as extreme gradient boosting (XGB), random forest, decision tree, logistic regression, and Naïve Bayes were applied. In this study, we used evaluation metrics, including the AUC ROC curve, accuracy, precision, recall, and F-measure, to assess the performance of the predictive models.

Result

In this study, a weighted sample of 16,832 under-five children was used in the final analysis. Among the proposed machine learning models, the random forest (RF) was the best-predicted model with an accuracy of 88.89%, a precision of 89.5%, an F-measure of 83%, an AUC ROC curve of 95.8%, and a recall of 77.6% in predicting the absence of mothers' healthcare-seeking behavior for ARIs. The accuracy for Naïve Bayes was the lowest (66.41%) when compared to other proposed models. No media exposure, living in rural areas, not breastfeeding, poor wealth status, home delivery, no ANC visit, no maternal education, mothers' age group of 35-49 years, and distance to health facilities were significant predictors for the absence of mothers' healthcare-seeking behaviors for ARIs. On the other hand, undernourished children with stunting, underweight, and wasting status, diarrhea, birth size, married women, being a male or female sex child, and having a maternal occupation were significantly associated with good maternal healthcare-seeking behaviors for ARIs among under-five children.

Conclusion

The RF model provides greater predictive power for estimating mothers' healthcare-seeking behaviors based on ARI risk factors. Machine learning could help achieve early prediction and intervention in children with high-risk ARIs. This leads to a recommendation for policy direction to reduce child mortality due to ARIs in sub-Saharan countries.

How Diet and Lifestyle Can Fine-Tune Gut Microbiomes for Healthy Aging

Many physical, social, and psychological changes occur during aging that raise the risk of developing chronic diseases, frailty, and dependency. These changes adversely affect the gut microbiota, a phenomenon known as microbe-aging. Those microbiota alterations are, in turn, associated with the development of age-related diseases. The gut microbiota is highly responsive to lifestyle and dietary changes, displaying a flexibility that also provides anactionable tool by which healthy aging can be promoted. This review covers, firstly, the main lifestyle and socioeconomic factors that modify the gut microbiota composition and function during healthy or unhealthy aging and, secondly, the advances being made in defining and promoting healthy aging, including microbiome-informed artificial intelligence tools, personalized dietary patterns, and food probiotic systems.

Unraveling the Potential of Attentive Bi-LSTM for Accurate Obesity Prognosis: Advancing Public Health towards Sustainable Cities

The global prevalence of obesity presents a pressing challenge to public health and healthcare systems, necessitating accurate prediction and understanding for effective prevention and management strategies. This article addresses the need for improved obesity prediction models by conducting a comprehensive analysis of existing machine learning (ML) and deep learning (DL) approaches. This study introduces a novel hybrid model, Attention-based Bi-LSTM (ABi-LSTM), which integrates attention mechanisms with bidirectional Long Short-Term Memory (Bi-LSTM) networks to enhance interpretability and performance in obesity prediction. Our study fills a crucial gap by bridging healthcare and urban planning domains, offering insights into data-driven approaches to promote healthier living within urban environments. The proposed ABi-LSTM model demonstrates exceptional performance, achieving a remarkable accuracy of 96.5% in predicting obesity levels. Comparative analysis showcases its superiority over conventional approaches, with superior precision, recall, and overall classification balance. This study highlights significant advancements in predictive accuracy and positions the ABi-LSTM model as a pioneering solution for accurate obesity prognosis. The implications extend beyond healthcare, offering a precise tool to address the global obesity epidemic and foster sustainable development in smart cities.

Identification of red blood cell distribution width as a prognostic factor in acute myeloid leukemia

Many prognostic factors have been identified in acute myeloid leukemia (AML). In this study, we investigated novel prognostic biomarkers using machine learning and Cox regression models in a prospective cohort of 591 patients with AML and tried to identify potential therapeutic targets based on transcriptomic data. We found that elevated red blood cell distribution width (RDW) at diagnosis was an adverse prognostic factor for AML, independent of the 2022 European LeukemiaNet (ELN2022) genetic risk. As a continuous variable, higher RDW was associated with shorter overall survival (OS) (hazard ratio [HR] 1.087, 95% confidence interval [CI] 1.036-1.139, p < 0.001) and event-free survival (EFS) (HR 1.078, 95% CI 1.033-1.124, p < 0.001). Elevated RDW returned to normal after consolidation therapy, which indicated that leukemia cells resulted in abnormal RDW. We further investigated the relationship between RDW and transcriptome in another cohort of 191 patients with AML and public datasets using gene set enrichment analysis (GSEA) and cell-type identification by estimating relative subsets of RNA transcripts (CIBERSORT). We found that patients in the high-RDW group were significantly enriched in the positive regulation of erythroid differentiation and inflammation-related pathways. Finally, we identified the inflammation-associated gene IL12RB2 and verified its prognostic relevance with patients with AML in public databases, suggesting it as a potential therapy target.

Randomized feature selection based semi-supervised latent Dirichlet allocation for microbiome analysis

Health and disease are fundamentally influenced by microbial communities and their genes (the microbiome). An in-depth analysis of microbiome structure that enables the classification of individuals based on their health can be crucial in enhancing diagnostics and treatment strategies to improve the overall well-being of an individual. In this paper, we present a novel semi-supervised methodology known as Randomized Feature Selection based Latent Dirichlet Allocation (RFSLDA) to study the impact of the gut microbiome on a subject's health status. Since the data in our study consists of fuzzy health labels, which are self-reported, traditional supervised learning approaches may not be suitable. As a first step, based on the similarity between documents in text analysis and gut-microbiome data, we employ Latent Dirichlet Allocation (LDA), a topic modeling approach which uses microbiome counts as features to group subjects into relatively homogeneous clusters, without invoking any knowledge of observed health status (labels) of subjects. We then leverage information from the observed health status of subjects to associate these clusters with the most similar health status making it a semi-supervised approach. Finally, a feature selection technique is incorporated into the model to improve the overall classification performance. The proposed method provides a semi-supervised topic modelling approach that can help handle the high dimensionality of the microbiome data in association studies. Our experiments reveal that our semi-supervised classification algorithm is effective and efficient in terms of high classification accuracy compared to popular supervised learning approaches like SVM and multinomial logistic model. The RFSLDA framework is attractive because it (i) enhances clustering accuracy by identifying key bacteria types as indicators of health status, (ii) identifies key bacteria types within each group based on estimates of the proportion of bacteria types within the groups, and (iii) computes a measure of within-group similarity to identify highly similar subjects in terms of their health status.

Past, present, and future of microbiome-based therapies

Technological advances in studying the human microbiome in depth have enabled the identification of microbial signatures associated with health and disease. This confirms the crucial role of microbiota in maintaining homeostasis and the host health status. Nowadays, there are several ways to modulate the microbiota composition to effectively improve host health; therefore, the development of therapeutic treatments based on the gut microbiota is experiencing rapid growth. In this review, we summarize the influence of the gut microbiota on the development of infectious disease and cancer, which are two of the main targets of microbiome-based therapies currently being developed. We analyze the two-way interaction between the gut microbiota and traditional drugs in order to emphasize the influence of gut microbial composition on drug effectivity and treatment response. We explore the different strategies currently available for modulating this ecosystem to our benefit, ranging from 1st generation intervention strategies to more complex 2nd generation microbiome-based therapies and their regulatory framework. Lastly, we finish with a quick overview of what we believe is the future of these strategies, that is 3rd generation microbiome-based therapies developed with the use of artificial intelligence (AI) algorithms.

Fast detection of bacterial gut pathogens on miniaturized devices: an overview

INTRODUCTION

Gut microbes pose challenges like colon inflammation, deadly diarrhea, antimicrobial resistance dissemination, and chronic disease onset. Development of early, rapid and specific diagnosis tools is essential for improving infection control. Point-of-care testing (POCT) systems offer rapid, sensitive, low-cost and sample-to-answer methods for microbe detection from various clinical and environmental samples, bringing the advantages of portability, automation, and simple operation.

AREAS COVERED

Rapid detection of gut microbes can be done using a wide array of techniques including biosensors, immunological assays, electrochemical impedance spectroscopy, mass spectrometry and molecular biology. Inclusion of Internet of Things, machine learning, and smartphone-based point-of-care applications is an important aspect of POCT. In this review, the authors discuss various fast diagnostic platforms for gut pathogens and their main challenges.

EXPERT OPINION

Developing effective assays for microbe detection can be complex. Assay design must consider factors like target selection, real-time and multiplex detection, sample type, reagent stability and storage, primer/probe design, and optimizing reaction conditions for accuracy and sensitivity. Mitigating these challenges requires interdisciplinary collaboration among scientists, clinicians, engineers, and industry partners. Future efforts are essential to enhance sensitivity, specificity, and versatility of POCT systems for gut microbe detection and quantification, advancing infectious disease diagnostics and management.

Harnessing of Artificial Intelligence for the Diagnosis and Prevention of Hospital-Acquired Infections: A Systematic Review

Healthcare-associated infections (HAIs) are the most common adverse events in healthcare and constitute a major global public health concern. Surveillance represents the foundation for the effective prevention and control of HAIs, yet conventional surveillance is costly and labor intensive. Artificial intelligence (AI) and machine learning (ML) have the potential to support the development of HAI surveillance algorithms for the understanding of HAI risk factors, the improvement of patient risk stratification as well as the prediction and timely detection and prevention of infections. AI-supported systems have so far been explored for clinical laboratory testing and imaging diagnosis, antimicrobial resistance profiling, antibiotic discovery and prediction-based clinical decision support tools in terms of HAIs. This review aims to provide a comprehensive summary of the current literature on AI applications in the field of HAIs and discuss the future potentials of this emerging technology in infection practice. Following the PRISMA guidelines, this study examined the articles in databases including PubMed and Scopus until November 2023, which were screened based on the inclusion and exclusion criteria, resulting in 162 included articles. By elucidating the advancements in the field, we aim to highlight the potential applications of AI in the field, report related issues and shortcomings and discuss the future directions.

Nine (not so simple) steps: a practical guide to using machine learning in microbial ecology

Due to the complex nature of microbiome data, the field of microbial ecology has many current and potential uses for machine learning (ML) modeling. With the increased use of predictive ML models across many disciplines, including microbial ecology, there is extensive published information on the specific ML algorithms available and how those algorithms have been applied. Thus, our goal is not to summarize the breadth of ML models available or compare their performances. Rather, our goal is to provide more concrete and actionable information to guide microbial ecologists in how to select, run, and interpret ML algorithms to predict the taxa or genes associated with particular sample categories or environmental gradients of interest. Such microbial data often have unique characteristics that require careful consideration of how to apply ML models and how to interpret the associated results. This review is intended for practicing microbial ecologists who may be unfamiliar with some of the intricacies of ML models. We provide examples and discuss common opportunities and pitfalls specific to applying ML models to the types of data sets most frequently collected by microbial ecologists.

A comprehensive overview of microbiome data in the light of machine learning applications: categorization, accessibility, and future directions

Metagenomics, Metabolomics, and Metaproteomics have significantly advanced our knowledge of microbial communities by providing culture-independent insights into their composition and functional potential. However, a critical challenge in this field is the lack of standard and comprehensive metadata associated with raw data, hindering the ability to perform robust data stratifications and consider confounding factors. In this comprehensive review, we categorize publicly available microbiome data into five types: shotgun sequencing, amplicon sequencing, metatranscriptomic, metabolomic, and metaproteomic data. We explore the importance of metadata for data reuse and address the challenges in collecting standardized metadata. We also, assess the limitations in metadata collection of existing public repositories collecting metagenomic data. This review emphasizes the vital role of metadata in interpreting and comparing datasets and highlights the need for standardized metadata protocols to fully leverage metagenomic data's potential. Furthermore, we explore future directions of implementation of Machine Learning (ML) in metadata retrieval, offering promising avenues for a deeper understanding of microbial communities and their ecological roles. Leveraging these tools will enhance our insights into microbial functional capabilities and ecological dynamics in diverse ecosystems. Finally, we emphasize the crucial metadata role in ML models development.

sscNOVA: a semi-supervised convolutional neural network for predicting functional regulatory variants in autoimmune diseases

Genome-wide association studies (GWAS) have identified thousands of variants in the human genome with autoimmune diseases. However, identifying functional regulatory variants associated with autoimmune diseases remains challenging, largely because of insufficient experimental validation data. We adopt the concept of semi-supervised learning by combining labeled and unlabeled data to develop a deep learning-based algorithm framework, sscNOVA, to predict functional regulatory variants in autoimmune diseases and analyze the functional characteristics of these regulatory variants. Compared to traditional supervised learning methods, our approach leverages more variants' data to explore the relationship between functional regulatory variants and autoimmune diseases. Based on the experimentally curated testing dataset and evaluation metrics, we find that sscNOVA outperforms other state-of-the-art methods. Furthermore, we illustrate that sscNOVA can help to improve the prioritization of functional regulatory variants from lead single-nucleotide polymorphisms and the proxy variants in autoimmune GWAS data.