Omics, big data and machine learning as tools to propel understanding of biological mechanisms and to discover novel diagnostics and therapeutics

Long noncoding RNAs in acute myeloid leukemia: biomarkers, prognostic indicators, and treatment potential

Long noncoding RNAs (lncRNAs) have been recognized as significant modulators of gene expression and are essential for various biological functions, even though they don't appear to have the ability to encode proteins. Originally considered dark matter, lncRNAs have been recognized as being dysregulated and contributing to the onset, progression, and resistance to treatment of acute myeloid leukemia (AML). AML is a prevalent type of leukemia characterized by the disruption of myeloid cell differentiation, leading to an increased number of immature myeloid progenitor cells. Currently, the need for novel biomarkers and treatment targets to enhance therapeutic alternatives has led to a focus on lncRNAs as possible indicators for prognostic, therapeutic, and diagnostic systems in various human cancers, including AML. Recent research has recognized a limited set of lncRNAs as possible prognostic biomarkers or diagnoses in AML. This review evaluates the key research that highlights the significance of lncRNAs in AML and discusses their roles and impacts on the disease. Furthermore, we intend to underscore the importance of lncRNAs as new and trustworthy markers for the diagnosis, prediction, drug resistance, and targets for treatment in AML.

Attention-based deep learning for accurate cell image analysis

High-content analysis (HCA) holds enormous potential for drug discovery and research, but widely used methods can be cumbersome and yield inaccurate results. Noisy and redundant signals in cell images impede accurate deep learning-based image analysis. To address these issues, we introduce X-Profiler, a novel HCA method that combines cellular experiments, image processing, and deep learning modeling. X-Profiler combines the convolutional neural network and Transformer to encode high-content images, effectively filtering out noisy signals and precisely characterizing cell phenotypes. In comparative tests on drug-induced cardiotoxicity, mitochondrial toxicity classification, and compound classification, X-Profiler outperformed both DeepProfiler and CellProfiler, as two highly recognized and representative methods in this field. Our results demonstrate the utility and versatility of X-Profiler, and we anticipate its wide application in HCA for advancing drug development and disease research.

Disruption of BCAA degradation is a critical characteristic of diabetic cardiomyopathy revealed by integrated transcriptome and metabolome analysis

In this study, we integrated transcriptomic and metabolomic analyses to achieve a comprehensive understanding of the underlying mechanisms of diabetic cardiomyopathy (DCM) in a diabetic rat model. Functional and molecular characterizations revealed significant cardiac injury, dysfunction, and ventricular remodeling in DCM. A thorough analysis of global changes in genes and metabolites showed that amino acid metabolism, especially the breakdown of branched-chain amino acids (BCAAs) such as valine, leucine, and isoleucine, is highly dysregulated. Furthermore, the study identified the transcription factor Gata3 as a predicted negative regulator of the gene encoding the key enzyme for BCAA degradation. These findings suggest that the disruption of BCAA degradation is a critical characteristic of diabetic myocardial damage and indicate a potential role for Gata3 in the dysregulation of BCAA metabolism in the context of DCM.

Application of machine learning for mass spectrometry-based multi-omics in thyroid diseases

Thyroid diseases, including functional and neoplastic diseases, bring a huge burden to people's health. Therefore, a timely and accurate diagnosis is necessary. Mass spectrometry (MS) based multi-omics has become an effective strategy to reveal the complex biological mechanisms of thyroid diseases. The exponential growth of biomedical data has promoted the applications of machine learning (ML) techniques to address new challenges in biology and clinical research. In this review, we presented the detailed review of applications of ML for MS-based multi-omics in thyroid disease. It is primarily divided into two sections. In the first section, MS-based multi-omics, primarily proteomics and metabolomics, and their applications in clinical diseases are briefly discussed. In the second section, several commonly used unsupervised learning and supervised algorithms, such as principal component analysis, hierarchical clustering, random forest, and support vector machines are addressed, and the integration of ML techniques with MS-based multi-omics data and its application in thyroid disease diagnosis is explored.

Microalgal metabolic engineering facilitates precision nutrition and dietary regulation

Microalgae have gained considerable attention as promising candidates for precision nutrition and dietary regulation due to their versatile metabolic capabilities. This review innovatively applies system metabolic engineering to utilize microalgae for precision nutrition and sustainable diets, encompassing the construction of microalgal cell factories, cell cultivation and practical application of microalgae. Manipulating the metabolic pathways and key metabolites of microalgae through multi-omics analysis and employing advanced metabolic engineering strategies, including ZFNs, TALENs, and the CRISPR/Cas system, enhances the production of valuable bioactive compounds, such as omega-3 fatty acids, antioxidants, and essential amino acids. This work begins by providing an overview of the metabolic diversity of microalgae and their ability to thrive in diverse environmental conditions. It then delves into the principles and strategies of metabolic engineering, emphasizing the genetic modifications employed to optimize microalgal strains for enhanced nutritional content. Enhancing PSY, BKT, and CHYB benefits carotenoid synthesis, whereas boosting ACCase, fatty acid desaturases, and elongases promotes polyunsaturated fatty acid production. Here, advancements in synthetic biology, evolutionary biology and machine learning are discussed, offering insights into the precision and efficiency of metabolic pathway manipulation. Also, this review highlights the potential impact of microalgal precision nutrition on human health and aquaculture. The optimized microalgal strains could serve as sustainable and cost-effective sources of nutrition for both human consumption and aquaculture feed, addressing the growing demand for functional foods and environmentally friendly feed alternatives. The tailored microalgal strains are anticipated to play a crucial role in meeting the nutritional needs of diverse populations and contributing to sustainable food production systems.

Machine Learning and Omics Analysis in Aortic Aneurysm

Aortic aneurysm is a life-threatening condition and mechanisms underlying its formation and progression are still incompletely understood. Omics approach has brought new insights to identify a broad spectrum of biomarkers and better understand cellular and molecular pathways involved. Omics generate a large amount of data and several studies have highlighted that artificial intelligence (AI) and techniques such as machine learning (ML)/deep learning (DL) can be of use in analyzing such complex datasets. However, only a few studies have so far reported the use of ML/DL for omics analysis in aortic aneurysms. The aim of this study is to summarize recent advances on the use of ML/DL for omics analysis to decipher aortic aneurysm pathophysiology and develop patient-tailored risk prediction models. In the light of current knowledge, we discuss current limits and highlight future directions in the field.

A dual-radiomics model for overall survival prediction in early-stage NSCLC patient using pre-treatment CT images

Introduction

Radiation therapy (RT) is one of the primary treatment options for early-stage non-small cell lung cancer (ES-NSCLC). Therefore, accurately predicting the overall survival (OS) rate following radiotherapy is crucial for implementing personalized treatment strategies. This work aims to develop a dual-radiomics (DR) model to (1) predict 3-year OS in ES-NSCLC patients receiving RT using pre-treatment CT images, and (2) provide explanations between feature importanceand model prediction performance.

Methods

The publicly available TCIA Lung1 dataset with 132 ES-NSCLC patients received RT were studied: 89/43 patients in the under/over 3-year OS group. For each patient, two types of radiomic features were examined: 56 handcrafted radiomic features (HRFs) extracted within gross tumor volume, and 512 image deep features (IDFs) extracted using a pre-trained U-Net encoder. They were combined as inputs to an explainable boosting machine (EBM) model for OS prediction. The EBM's mean absolute scores for HRFs and IDFs were used as feature importance explanations. To evaluate identified feature importance, the DR model was compared with EBM using either (1) key or (2) non-key feature type only. Comparison studies with other models, including supporting vector machine (SVM) and random forest (RF), were also included. The performance was evaluated by the area under the receiver operating characteristic curve (AUCROC), accuracy, sensitivity, and specificity with a 100-fold Monte Carlo cross-validation.

Results

The DR model showed highestperformance in predicting 3-year OS (AUCROC=0.81 ± 0.04), and EBM scores suggested that IDFs showed significantly greater importance (normalized mean score=0.0019) than HRFs (score=0.0008). The comparison studies showed that EBM with key feature type (IDFs-only demonstrated comparable AUCROC results (0.81 ± 0.04), while EBM with non-key feature type (HRFs-only) showed limited AUCROC (0.64 ± 0.10). The results suggested that feature importance score identified by EBM is highly correlated with OS prediction performance. Both SVM and RF models were unable to explain key feature type while showing limited overall AUCROC=0.66 ± 0.07 and 0.77 ± 0.06, respectively. Accuracy, sensitivity, and specificity showed a similar trend.

Discussion

In conclusion, a DR model was successfully developed to predict ES-NSCLC OS based on pre-treatment CT images. The results suggested that the feature importance from DR model is highly correlated to the model prediction power.

Harnessing Artificial Intelligence in Multimodal Omics Data Integration: Paving the Path for the Next Frontier in Precision Medicine

The integration of multiomics data with detailed phenotypic insights from electronic health records marks a paradigm shift in biomedical research, offering unparalleled holistic views into health and disease pathways. This review delineates the current landscape of multimodal omics data integration, emphasizing its transformative potential in generating a comprehensive understanding of complex biological systems. We explore robust methodologies for data integration, ranging from concatenation-based to transformation-based and network-based strategies, designed to harness the intricate nuances of diverse data types. Our discussion extends from incorporating large-scale population biobanks to dissecting high-dimensional omics layers at the single-cell level. The review underscores the emerging role of large language models in artificial intelligence, anticipating their influence as a near-future pivot in data integration approaches. Highlighting both achievements and hurdles, we advocate for a concerted effort toward sophisticated integration models, fortifying the foundation for groundbreaking discoveries in precision medicine.

Maximizing Analytical Performance in Biomolecular Discovery with LC-MS: Focus on Psychiatric Disorders

In this review, we discuss the cutting-edge developments in mass spectrometry proteomics and metabolomics that have brought improvements for the identification of new disease-based biomarkers. A special focus is placed on psychiatric disorders, for example, schizophrenia, because they are considered to be not a single disease entity but rather a spectrum of disorders with many overlapping symptoms. This review includes descriptions of various types of commonly used mass spectrometry platforms for biomarker research, as well as complementary techniques to maximize data coverage, reduce sample heterogeneity, and work around potentially confounding factors. Finally, we summarize the different statistical methods that can be used for improving data quality to aid in reliability and interpretation of proteomics findings, as well as to enhance their translatability into clinical use and generalizability to new data sets.

A Systematic Review of Genetics- and Molecular-Pathway-Based Machine Learning Models for Neurological Disorder Diagnosis

The process of identification and management of neurological disorder conditions faces challenges, prompting the investigation of novel methods in order to improve diagnostic accuracy. In this study, we conducted a systematic literature review to identify the significance of genetics- and molecular-pathway-based machine learning (ML) models in treating neurological disorder conditions. According to the study's objectives, search strategies were developed to extract the research studies using digital libraries. We followed rigorous study selection criteria. A total of 24 studies met the inclusion criteria and were included in the review. We classified the studies based on neurological disorders. The included studies highlighted multiple methodologies and exceptional results in treating neurological disorders. The study findings underscore the potential of the existing models, presenting personalized interventions based on the individual's conditions. The findings offer better-performing approaches that handle genetics and molecular data to generate effective outcomes. Moreover, we discuss the future research directions and challenges, emphasizing the demand for generalizing existing models in real-world clinical settings. This study contributes to advancing knowledge in the field of diagnosis and management of neurological disorders.

Are we ready for genetic testing in metabolic dysfunction-associated steatotic liver disease?

Metabolic dysfunction-associated steatotic liver disease (MASLD), with its steadily increasing prevalence, represents now a major problem in public health. A proper referral could benefit from tools allowing more precise risk stratification. To this end, in recent decades, several genetic variants that may help predict and refine the risk of development and progression of MASLD have been investigated. In this review, we aim to discuss the role genetics in MASLD plays in everyday clinical practice. We performed a comprehensive literature search of PubMed for relevant publications. Available evidence highlights the emergence of genetic-based noninvasive algorithms for diagnosing fatty liver, metabolic dysfunction-associated steatohepatitis, fibrosis progression and occurrence of liver-related outcomes including hepatocellular carcinoma. Nevertheless, their accuracy is not optimal and application in everyday clinical practice remains challenging. Furthermore, susceptible genetic markers have recently become subjects of great scientific interest as therapeutic targets in precision medicine. In conclusion, decisional algorithms based on genetic testing in MASLD to facilitate the clinician decisions on management and treatment are under growing investigation and could benefit from artificial intelligence methodology.

Adjusting for covariates and assessing modeling fitness in machine learning using MUVR2

Motivation

Machine learning (ML) methods are frequently used in Omics research to examine associations between molecular data and for example exposures and health conditions. ML is also used for feature selection to facilitate biological interpretation. Our previous MUVR algorithm was shown to generate predictions and variable selections at state-of-the-art performance. However, a general framework for assessing modeling fitness is still lacking. In addition, enabling to adjust for covariates is a highly desired, but largely lacking trait in ML. We aimed to address these issues in the new MUVR2 framework.

Results

The MUVR2 algorithm was developed to include the regularized regression framework elastic net in addition to partial least squares and random forest modeling. Compared with other cross-validation strategies, MUVR2 consistently showed state-of-the-art performance, including variable selection, while minimizing overfitting. Testing on simulated and real-world data, we also showed that MUVR2 allows for the adjustment for covariates using elastic net modeling, but not using partial least squares or random forest.

Availability and implementation

Algorithms, data, scripts, and a tutorial are open source under GPL-3 license and available in the MUVR2 R package at https://github.com/MetaboComp/MUVR2.

Metabolomics of natural samples: A tutorial review on the latest technologies

Metabolomics is the study of metabolites present in a living system. It is a rapidly growing field aimed at discovering novel compounds, studying biological processes, diagnosing diseases, and ensuring the quality of food products. Recently, the analysis of natural samples has become important to explore novel bioactive compounds and to study how environment and genetics affect living systems. Various metabolomics techniques, databases, and data analysis tools are available for natural sample metabolomics. However, choosing the right method can be a daunting exercise because natural samples are heterogeneous and require untargeted approaches. This tutorial review aims to compile the latest technologies to guide an early-career scientist on natural sample metabolomics. First, different extraction methods and their pros and cons are reviewed. Second, currently available metabolomics databases and data analysis tools are summarized. Next, recent research on metabolomics of milk, honey, and microbial samples is reviewed. Finally, after reviewing the latest trends in technologies, a checklist is presented to guide an early-career researcher on how to design a metabolomics project. In conclusion, this review is a comprehensive resource for a researcher planning to conduct their first metabolomics analysis. It is also useful for experienced researchers to update themselves on the latest trends in metabolomics.

A computational biology approach to identify potential protein biomarkers and drug targets for sporadic amyotrophic lateral sclerosis

Amyotrophic lateral sclerosis (ALS) is a neurodegenerative disease caused by the loss of upper and lower motor neurons. The sporadic ALS (sALS) is a multigenic disorder and the complex mechanisms underlying its onset are still not fully delineated. Despite the recent scientific advancements, certain aspects of ALS pathogenic targets need to be yet clarified. The aim of the presented study is to identify potential genetic biomarkers and drug targets for sALS, by analysing gene expression profiles, presented in the publicly available GSE68605 dataset, of motor neurons cells obtained from sALS patients. We used different computational approaches including differential expression analysis, protein network mapping, candidate protein biomarker (CPB) identification, elucidation of the role of functional modules, and molecular docking analysis. The resultant top ten up- and downregulated genes were further used to construct protein-protein interaction network (PPIN). The PPIN analysis resulted in identifying four CPBs (namely RIOK2, AKT1, CTNNB1, and TNF) that commonly overlapped with one another in network parameters (degree, bottleneck and maximum neighbourhood component). The RIOK2 protein emerged as a potential mediator of top five functional modules that are associated with RNA binding, lipoprotein particle receptor binding in pre-ribosome, and interferon, cytokine-mediated signaling pathway. Furthermore, molecular docking analysis revealed that cyclosporine exhibited the highest binding affinity (-8.6 kJ/mol) with RIOK2, and surpassed the FDA-approved ALS drugs, such as riluzole and edaravone. This suggested that cyclosporine may serve as a promising candidate for targeting RIOK2 downregulation observed in sALS patients. In order to validate our computational results, it is suggested that in vitro and in vivo studies may be conducted in future to provide a more detailed understanding of ALS diagnosis, prognosis, and therapeutic intervention.

Multi-Omics Mining of lncRNAs with Biological and Clinical Relevance in Cancer

In this review, we provide a general overview of the current panorama of mining strategies for multi-omics data to investigate lncRNAs with an actual or potential role as biological markers in cancer. Several multi-omics studies focusing on lncRNAs have been performed in the past with varying scopes. Nevertheless, many questions remain regarding the pragmatic application of different molecular technologies and bioinformatics algorithms for mining multi-omics data. Here, we attempt to address some of the less discussed aspects of the practical applications using different study designs for incorporating bioinformatics and statistical analyses of multi-omics data. Finally, we discuss the potential improvements and new paradigms aimed at unraveling the role and utility of lncRNAs in cancer and their potential use as molecular markers for cancer diagnosis and outcome prediction.

dRFEtools: dynamic recursive feature elimination for omics

MOTIVATION

Advances in technology have generated larger omics datasets with potential applications for machine learning. In many datasets, however, cost and limited sample availability result in an excessively higher number of features as compared to observations. Moreover, biological processes are associated with networks of core and peripheral genes, while traditional feature selection approaches capture only core genes.

RESULTS

To overcome these limitations, we present dRFEtools that implements dynamic recursive feature elimination (RFE), reducing computational time with high accuracy compared to standard RFE, expanding dynamic RFE to regression algorithms, and outputting the subsets of features that hold predictive power with and without peripheral features. dRFEtools integrates with scikit-learn (the popular Python machine learning platform) and thus provides new opportunities for dynamic RFE in large-scale omics data while enhancing its interpretability.

AVAILABILITY AND IMPLEMENTATION

dRFEtools is freely available on PyPI at https://pypi.org/project/drfetools/ or on GitHub https://github.com/LieberInstitute/dRFEtools, implemented in Python 3, and supported on Linux, Windows, and Mac OS.

Perspective of artificial intelligence in healthcare data management: A journey towards precision medicine

Mounting evidence has highlighted the implementation of big data handling and management in the healthcare industry to improve the clinical services. Various private and public companies have generated, stored, and analyzed different types of big healthcare data, such as omics data, clinical data, electronic health records, personal health records, and sensing data with the aim to move in the direction of precision medicine. Additionally, with the advancement in technologies, researchers are curious to extract the potential involvement of artificial intelligence and machine learning on big healthcare data to enhance the quality of patient's lives. However, seeking solutions from big healthcare data requires proper management, storage, and analysis, which imposes hinderances associated with big data handling. Herein, we briefly discuss the implication of big data handling and the role of artificial intelligence in precision medicine. Further, we also highlighted the potential of artificial intelligence in integrating and analyzing the big data that offer personalized treatment. In addition, we briefly discuss the applications of artificial intelligence in personalized treatment, especially in neurological diseases. Lastly, we discuss the challenges and limitations imposed by artificial intelligence in big data management and analysis to hinder precision medicine.

Health inequity in genomic personalized medicine in underrepresented populations: a look at the current evidence

Improvements in sequencing technology coupled with dramatic declines in the cost of genome sequencing have led to a proportional growth in the size and number of genetic datasets since the release of the human genetic sequence by The Human Genome Project (HGP) international consortium. The HGP was undeniably a significant scientific success, a turning point in human genetics and the beginning of human genomics. This burst of genetic information has led to a greater understanding of disease pathology and the potential of employing this data to deliver more precise patient care. Hence, the recognition of high-penetrance disease-causing mutations which encode drivers of disease has made the management of most diseases more specific. Nonetheless, while genetic scores are becoming more extensively used, their application in the real world is expected to be limited due to the lack of diversity in the data used to construct them. Underrepresented populations, such as racial and ethnic minorities, low-income individuals, and those living in rural areas, often experience greater health disparities and worse health outcomes compared to the general population. These disparities are often the result of systemic barriers, such as poverty, discrimination, and limited access to healthcare. Addressing health inequity in underrepresented populations requires addressing the underlying social determinants of health and implementing policies and programs which promoted health equity and reduce disparities. This can include expanding access to affordable healthcare, addressing poverty and unemployment, and promoting policies that combat discrimination and racism.

Elucidating the functional roles of prokaryotic proteins using big data and artificial intelligence

Annotating protein sequences according to their biological functions is one of the key steps in understanding microbial diversity, metabolic potentials, and evolutionary histories. However, even in the best-studied prokaryotic genomes, not all proteins can be characterized by classical in vivo, in vitro, and/or in silico methods-a challenge rapidly growing alongside the advent of next-generation sequencing technologies and their enormous extension of 'omics' data in public databases. These so-called hypothetical proteins (HPs) represent a huge knowledge gap and hidden potential for biotechnological applications. Opportunities for leveraging the available 'Big Data' have recently proliferated with the use of artificial intelligence (AI). Here, we review the aims and methods of protein annotation and explain the different principles behind machine and deep learning algorithms including recent research examples, in order to assist both biologists wishing to apply AI tools in developing comprehensive genome annotations and computer scientists who want to contribute to this leading edge of biological research.

Latent disease similarities and therapeutic repurposing possibilities uncovered by multi-modal generative topic modeling of human diseases

Motivation

Human diseases are characterized by multiple features such as their pathophysiological, molecular and genetic changes. The rapid expansion of such multi-modal disease-omics space provides an opportunity to re-classify diverse human diseases and to uncover their latent molecular similarities, which could be exploited to repurpose a therapeutic-target for one disease to another.

Results

Herein, we probe this underexplored space by soft-clustering 6955 human diseases by multi-modal generative topic modeling. Focusing on chronic kidney disease and myocardial infarction, two most life-threatening diseases, unveiled are their previously underrecognized molecular similarities to neoplasia and mental/neurological-disorders, and 69 repurposable therapeutic-targets for these diseases. Using an edit-distance-based pathway-classifier, we also find molecular pathways by which these targets could elicit their clinical effects. Importantly, for the 17 targets, the evidence for their therapeutic usefulness is retrospectively found in the pre-clinical and clinical space, illustrating the effectiveness of the method, and suggesting its broader applications across diverse human diseases.

Availability and implementation

The code reported in this article is available at: https://github.com/skozawa170301ktx/MultiModalDiseaseModeling.

Supplementary information

Supplementary data are available at Bioinformatics Advances online.

Early metabolomic, lipid and lipoprotein changes in response to medical and surgical therapeutic approaches to obesity

BACKGROUND

Glucagon-like peptide-1 receptor agonists (GLP-1RA) and bariatric surgery have proven to be effective treatments for obesity and cardiometabolic conditions. We aimed to explore the early metabolomic changes in response to GLP-1RA (liraglutide) therapy vs. placebo and in comparison to bariatric surgery.

METHODS

Three clinical studies were conducted: a bariatric surgery cohort study of participants with morbid obesity who underwent either Roux-en-Y gastric bypass (RYGB) or sleeve gastrectomy (SG) studied over four and twelve weeks, and two randomized placebo-controlled, crossover double blind studies of liraglutide vs. placebo administration in participants with type 2 diabetes (T2D) and participants with obesity studied for three and five weeks, respectively. Nuclear magnetic resonance spectroscopy-derived metabolomic data were assessed in all eligible participants who completed all the scheduled in-clinic visits. The primary outcome of the study was to explore the changes of the metabolome among participants with obesity with and without T2D receiving the GLP-1RA liraglutide vs. placebo and participants with obesity undergoing bariatric surgery during the three to five-week study period. In addition, we assessed the bariatric surgery effects longitudinally over the twelve weeks of the study and the differences between the bariatric surgery subgroups on the metabolome. The trials are registered with ClinicalTrials.gov, numbers NCT03851874, NCT01562678 and NCT02944500.

RESULTS

Bariatric surgery had a more pronounced effect on weight and body mass index reduction (-14.19 ± 5.27 kg and - 5.19 ± 5.27, respectively, p < 0.001 for both) and resulted in more pronounced metabolomic and lipidomic changes compared to liraglutide therapy at four weeks postoperatively. Significant changes were observed in lipoprotein parameters, inflammatory markers, ketone bodies, citrate, and branched-chain amino acids after the first three to five weeks of intervention. After adjusting for the amount of weight loss, a significant difference among the study groups remained only for acetoacetate, β-hydroxybutyrate, and citrate (p < 0.05 after FDR correction). Glucose levels were significantly reduced in all intervention groups but mainly in the T2D group receiving GLP-1RA treatment. After adjusting for weight loss, only glucose levels remained significant (p = 0.001 after FDR correction), mainly due to the glucose change in the T2D group receiving GLP-1RA. Similar results with those observed at four weeks were observed in the surgical group when delta changes at twelve weeks were assessed. Comparing the two types of bariatric surgery, an intervention effect was more pronounced in the RYGB subgroup regarding total triglycerides, triglyceride-rich lipoprotein size, and trimethylamine-N-oxide (p for intervention: 0.031, 0.028, 0.036, respectively). However, after applying FDR correction, these changes deemed to be only suggestive; only time effects remained significant with no significant changes persisting in relation to the types of bariatric surgery.

CONCLUSIONS

The results of this study suggest that the early metabolomic, lipid and lipoprotein changes observed between liraglutide treatment and bariatric surgery are similar and result largely from the changes in patients' body weight. Specific changes observed in the short-term post-surgical period between bariatric vs. nonsurgical treated participants, i.e., acetoacetate, β-hydroxybutyrate, and citrate changes, may reflect changes in patient diets and calorie intake indicating potential calorie and diet-driven metabolomics/lipidomic effects in the short-term postoperatively. Significant differences observed between SG and RYGB need to be confirmed and extended by future studies.

Recent advances in machine learning applications in metabolic engineering

Metabolic engineering encompasses several widely-used strategies, which currently hold a high seat in the field of biotechnology when its potential is manifesting through a plethora of research and commercial products with a strong societal impact. The genomic revolution that occurred almost three decades ago has initiated the generation of large omics-datasets which has helped in gaining a better understanding of cellular behavior. The itinerary of metabolic engineering that has occurred based on these large datasets has allowed researchers to gain detailed insights and a reasonable understanding of the intricacies of biosystems. However, the existing trail-and-error approaches for metabolic engineering are laborious and time-intensive when it comes to the production of target compounds with high yields through genetic manipulations in host organisms. Machine learning (ML) coupled with the available metabolic engineering test instances and omics data brings a comprehensive and multidisciplinary approach that enables scientists to evaluate various parameters for effective strain design. This vast amount of biological data should be standardized through knowledge engineering to train different ML models for providing accurate predictions in gene circuits designing, modification of proteins, optimization of bioprocess parameters for scaling up, and screening of hyper-producing robust cell factories. This review briefs on the premise of ML, followed by mentioning various ML methods and algorithms alongside the numerous omics datasets available to train ML models for predicting metabolic outcomes with high-accuracy. The combinative interplay between the ML algorithms and biological datasets through knowledge engineering have guided the recent advancements in applications such as CRISPR/Cas systems, gene circuits, protein engineering, metabolic pathway reconstruction, and bioprocess engineering. Finally, this review addresses the probable challenges of applying ML in metabolic engineering which will guide the researchers toward novel techniques to overcome the limitations.

Essential Role of Multi-Omics Approaches in the Study of Retinal Vascular Diseases

Retinal vascular disease is a highly prevalent vision-threatening ocular disease in the global population; however, its exact mechanism remains unclear. The expansion of omics technologies has revolutionized a new medical research methodology that combines multiple omics data derived from the same patients to generate multi-dimensional and multi-evidence-supported holistic inferences, providing unprecedented opportunities to elucidate the information flow of complex multi-factorial diseases. In this review, we summarize the applications of multi-omics technology to further elucidate the pathogenesis and complex molecular mechanisms underlying retinal vascular diseases. Moreover, we proposed multi-omics-based biomarker and therapeutic strategy discovery methodologies to optimize clinical and basic medicinal research approaches to retinal vascular diseases. Finally, the opportunities, current challenges, and future prospects of multi-omics analyses in retinal vascular disease studies are discussed in detail.

Machine Learning in Nutrition Research

Data currently generated in the field of nutrition are becoming increasingly complex and high-dimensional, bringing with them new methods of data analysis. The characteristics of machine learning (ML) make it suitable for such analysis and thus lend itself as an alternative tool to deal with data of this nature. ML has already been applied in important problem areas in nutrition, such as obesity, metabolic health, and malnutrition. Despite this, experts in nutrition are often without an understanding of ML, which limits its application and therefore potential to solve currently open questions. The current article aims to bridge this knowledge gap by supplying nutrition researchers with a resource to facilitate the use of ML in their research. ML is first explained and distinguished from existing solutions, with key examples of applications in the nutrition literature provided. Two case studies of domains in which ML is particularly applicable, precision nutrition and metabolomics, are then presented. Finally, a framework is outlined to guide interested researchers in integrating ML into their work. By acting as a resource to which researchers can refer, we hope to support the integration of ML in the field of nutrition to facilitate modern research.

Discovering driver nodes in chronic kidney disease-related networks using Trader as a newly developed algorithm

Thanks to the advances in the field of computational-based biology, a huge volume of disease-related data has been generated so far. From the existing data, the disease-related protein-protein interaction (PPI) networks seem to yield effective treatment plans due to the informative/systematic representation of diseases. Yet, a large number of previous studies have failed due to the complex nature of such disease-related networks. For addressing this limitation, in the present study, we combined Trader and the DFS algorithms to identify a minimal subset of nodes (driver nodes) whose removal produces a maximum number of disjoint sub-networks. We then screened the nodes in the disease-associated PPI networks and to evaluate the efficiency of the suggested method, it was applied to six PPI networks of differentially expressed genes in chronic kidney diseases. The performance of Trader was superior to other well-known algorithms in terms of identifying driver nodes. Besides, the proportion of proteins that were targeted by at least one FDA-approved drug was significantly higher among the identified driver nodes when compared with the rest of the proteins in the networks. The proposed algorithm could be applied for predicting future therapeutic targets in complex disorder networks. In conclusion, unlike the common methods, computationally efficient algorithms can generate more practical outcomes which are compatible with real-world biological facts.

Big Data in Laboratory Medicine—FAIR Quality for AI?

Laboratory medicine is a digital science. Every large hospital produces a wealth of data each day—from simple numerical results from, e.g., sodium measurements to highly complex output of “-omics” analyses, as well as quality control results and metadata. Processing, connecting, storing, and ordering extensive parts of these individual data requires Big Data techniques. Whereas novel technologies such as artificial intelligence and machine learning have exciting application for the augmentation of laboratory medicine, the Big Data concept remains fundamental for any sophisticated data analysis in large databases. To make laboratory medicine data optimally usable for clinical and research purposes, they need to be FAIR: findable, accessible, interoperable, and reusable. This can be achieved, for example, by automated recording, connection of devices, efficient ETL (Extract, Transform, Load) processes, careful data governance, and modern data security solutions. Enriched with clinical data, laboratory medicine data allow a gain in pathophysiological insights, can improve patient care, or can be used to develop reference intervals for diagnostic purposes. Nevertheless, Big Data in laboratory medicine do not come without challenges: the growing number of analyses and data derived from them is a demanding task to be taken care of. Laboratory medicine experts are and will be needed to drive this development, take an active role in the ongoing digitalization, and provide guidance for their clinical colleagues engaging with the laboratory data in research.

Role of Omics in Migraine Research and Management: A Narrative Review

Migraine is a neurological disorder defined by episodic attacks of chronic pain associated with nausea, photophobia, and phonophobia. It is known to be a complex disease with several environmental and genetic factors contributing to its susceptibility. Risk factors for migraine include head or neck injury (Arnold, Cephalalgia 38(1):1-211, 2018). Stress and high temperature are known to trigger migraine, while sleep disorders and anxiety are considered to be the comorbid conditions with migraine. Studies have reported various biomarkers, including genetic variants, proteins, and metabolites implicated in migraine's pathophysiology. Using the "omics" approach, which deals with genetics, transcriptomics, proteomics, and metabolomics, more specific biomarkers for various migraine can be identified. On account of its multifactorial nature, migraine is an ideal study model focusing on integrated omics approaches, including genomics, transcriptomics, proteomics, and metabolomics. The current review has been compiled with an aim to focus on the genomic alterations especially involved in the regulation of glutamatergic neurotransmission, cortical excitability, ion channels, solute carrier proteins, or receptors; their expression in migraine patients and also specific proteins and metabolites, including some inflammatory biomarkers that might represent the migraine phenotype at the molecular level. The systems biology approach holds the promise to understand the pathophysiology of the disease at length and also to identify the specific therapeutic targets for novel interventions.

The pharmacogenomics of selective serotonin reuptake inhibitors

Antidepressant medications are frequently used as the first line of treatment for depression. However, their effectiveness is highly variable and influenced by genetic factors. Recently, pharmacogenetic studies, including candidate-gene, genome-wide association studies or polygenic risk scores, have attempted to uncover the genetic architecture of antidepressant response. Genetic variants in at least 27 genes are linked to antidepressant treatment response in both coding and non-coding genomic regions, but evidence is largely inconclusive due to the high polygenicity of the trait and limited cohort sizes in published studies. Future studies should increase the number and diversity of participants to yield sufficient statistical power to characterize the genetic underpinnings and biological mechanisms of treatment response, improve results generalizability and reduce racial health-related inequities.

Artificial intelligence in cancer target identification and drug discovery

Artificial intelligence is an advanced method to identify novel anticancer targets and discover novel drugs from biology networks because the networks can effectively preserve and quantify the interaction between components of cell systems underlying human diseases such as cancer. Here, we review and discuss how to employ artificial intelligence approaches to identify novel anticancer targets and discover drugs. First, we describe the scope of artificial intelligence biology analysis for novel anticancer target investigations. Second, we review and discuss the basic principles and theory of commonly used network-based and machine learning-based artificial intelligence algorithms. Finally, we showcase the applications of artificial intelligence approaches in cancer target identification and drug discovery. Taken together, the artificial intelligence models have provided us with a quantitative framework to study the relationship between network characteristics and cancer, thereby leading to the identification of potential anticancer targets and the discovery of novel drug candidates.

Genetic Analysis of Coronary Artery Disease Using Tree-Based Automated Machine Learning Informed By Biology-Based Feature Selection

Machine Learning (ML) approaches are increasingly being used in biomedical applications. Important challenges of ML include choosing the right algorithm and tuning the parameters for optimal performance. Automated ML (AutoML) methods, such as Tree-based Pipeline Optimization Tool (TPOT), have been developed to take some of the guesswork out of ML thus making this technology available to users from more diverse backgrounds. The goals of this study were to assess applicability of TPOT to genomics and to identify combinations of single nucleotide polymorphisms (SNPs) associated with coronary artery disease (CAD), with a focus on genes with high likelihood of being good CAD drug targets. We leveraged public functional genomic resources to group SNPs into biologically meaningful sets to be selected by TPOT. We applied this strategy to data from the U.K. Biobank, detecting a strikingly recurrent signal stemming from a group of 28 SNPs. Importance analysis of these SNPs uncovered functional relevance of the top SNPs to genes whose association with CAD is supported in the literature and other resources. Furthermore, we employed game-theory based metrics to study SNP contributions to individual-level TPOT predictions and discover distinct clusters of well-predicted CAD cases. The latter indicates a promising approach towards precision medicine.

Blood Viscosity in Subjects With Type 2 Diabetes Mellitus: Roles of Hyperglycemia and Elevated Plasma Fibrinogen

The viscosity of blood is an indicator in the understanding and treatment of disease. An elevated blood viscosity has been demonstrated in patients with Type 2 Diabetes Mellitus (T2DM), which might represent a risk factor for cardiovascular complications. However, the roles of glycated hemoglobin (HbA1c) and plasma fibrinogen levels on the elevated blood viscosity in subjects with T2DM at different chronic glycemic conditions are still not clear. Here, we evaluate the relationship between the blood viscosity and HbA1c as well as plasma fibrinogen levels in patients with T2DM. The experimental data show that the mean values of the T2DM blood viscosity are higher in groups with higher HbA1c levels, but the correlation between the T2DM blood viscosity and the HbA1c level is not obvious. Instead, when we investigate the influence of plasma fibrinogen level on the blood viscosity in T2DM subjects, we find that the T2DM blood viscosity is significantly and positively correlated with the plasma fibrinogen level. Further, to probe the combined effects of multiple factors (including the HbA1c and plasma fibrinogen levels) on the altered blood viscosity in T2DM, we regroup the experimental data based on the T2DM blood viscosity values at both the low and high shear rates, and our results suggest that the influence of the elevated HbA1c level on blood viscosity is quite limited, although it is an important indicator of glycemic control in T2DM patients. Instead, the elevated blood hematocrit, the enhanced red blood cell (RBC) aggregation induced by the increased plasma fibrinogen level, and the reduced RBC deformation play key roles in the determination of blood viscosity in T2DM. Together, these experimental results are helpful in identifying the key determinants for the altered T2DM blood viscosity, which can be used in future studies of the hemorheological disturbances of T2DM patients.

Nanomedicine Ex Machina: Between Model-Informed Development and Artificial Intelligence

Today, a growing number of computational aids and simulations are shaping model-informed drug development. Artificial intelligence, a family of self-learning algorithms, is only the latest emerging trend applied by academic researchers and the pharmaceutical industry. Nanomedicine successfully conquered several niche markets and offers a wide variety of innovative drug delivery strategies. Still, only a small number of patients benefit from these advanced treatments, and the number of data sources is very limited. As a consequence, “big data” approaches are not always feasible and smart combinations of human and artificial intelligence define the research landscape. These methodologies will potentially transform the future of nanomedicine and define new challenges and limitations of machine learning in their development. In our review, we present an overview of modeling and artificial intelligence applications in the development and manufacture of nanomedicines. Also, we elucidate the role of each method as a facilitator of breakthroughs and highlight important limitations.

Application of omics beyond the central dogma in coronary heart disease research: A bibliometric study and literature review

Despite remarkable progress in disease diagnosis and treatment, coronary heart disease (CHD) remains the number one leading cause of death worldwide. Many practical challenges still faced in clinical settings necessitates the pursuit of omics studies to identify alternative/orthogonal biomarkers, as well as to discover novel insights into disease mechanisms. Albeit relatively nascent as compared to the omics frontrunners (genomics, transcriptomics, and proteomics), omics beyond the central dogma (OBCD; e.g., metabolomics, lipidomics, glycomics, and metallomics) have undeniable contributions and prospects in CHD research. In this bibliometric study, we characterised the global trends in publication/citation outputs, collaborations, and research hotspots concerning OBCD-CHD, with a focus on the more prolific fields of metabolomics and lipidomics. As for glycomics and metallomics, there were insufficient publication records on their applications in CHD research for quantitative bibliometrics analysis. Thus, we reviewed their applications in health/disease research in general, discussed and justified their potential in CHD research, and suggested important/promising research avenues. By summarising evidence obtained both quantitatively and qualitatively, this study offers a first and comprehensive picture of OBCD applications in CHD, facilitating the establishment of future research directions.

Recent advances in mass-spectrometry based proteomics software, tools and databases

The field of proteomics immensely depends on data generation and data analysis which are thoroughly supported by software and databases. There has been a massive advancement in mass spectrometry-based proteomics over the last 10 years which has compelled the scientific community to upgrade or develop algorithms, tools, and repository databases in the field of proteomics. Several standalone software, and comprehensive databases have aided the establishment of integrated omics pipeline and meta-analysis workflow which has contributed to understand the disease pathobiology, biomarker discovery and predicting new therapeutic modalities. For shotgun proteomics where Data Dependent Acquisition is performed, several user-friendly software are developed that can analyse the pre-processed data to provide mechanistic insights of the disease. Likewise, in Data Independent Acquisition, pipelines are emerged which can accomplish the task from building the spectral library to identify the therapeutic targets. Furthermore, in the age of big data analysis the implications of machine learning and cloud computing are appending robustness, rapidness and in-depth proteomics data analysis. The current review talks about the recent advancement, and development of software, tools, and database in the field of mass-spectrometry based proteomics.

Epione application: An integrated web‑toolkit of clinical genomics and personalized medicine in systemic lupus erythematosus

Genome wide association studies (GWAS) have identified autoimmune disease-associated loci, a number of which are involved in numerous disease-associated pathways. However, much of the underlying genetic and pathophysiological mechanisms remain to be elucidated. Systemic lupus erythematosus (SLE) is a chronic, highly heterogeneous auto-immune disease, characterized by differences in autoantibody profile, serum cytokines and a multi-system involvement. This study presents the Epione application, an integrated bioinformatics web-toolkit, designed to assist medical experts and researchers in more accurately diagnosing SLE. The application aims to identify the most credible gene variants and single nucleotide polymorphisms (SNPs) associated with SLE susceptibility, by using patient's genomic data to aid the medical expert in SLE diagnosis. The application contains useful knowledge of >70,000 SLE-related publications that have been analyzed, using data mining and semantic techniques, towards extracting the SLE-related genes and the corresponding SNPs. Probable genes associated with the patient's genomic profile are visualized with several graphs, including chromosome ideograms, statistic bars and regulatory networks through data mining studies with relative publications, to obtain a representative number of the most credible candidate genes and biological pathways associated with the SLE. Furthermore, an evaluation study was performed on a patient diagnosed with SLE and is presented herein. Epione has also been expanded in family-related candidate patients to evaluate its predictive power. All the recognized gene variants that were previously considered to be associated with SLE were accurately identified in the output profile of the patient, and by comparing the results, novel findings have emerged. The Epione application may assist and facilitate in early stage diagnosis by using the patients' genomic profile to compare against the list of the most predictable candidate gene variants related to SLE. Its diagnosis-oriented output presents the user with a structured set of results on variant association, position in genome and links to specific bibliography and gene network associations. The overall aim of the present study was to provide a reliable tool for the most effective study of SLE. This novel and accessible webserver tool of SLE is available at http://geneticslab.aua.gr/epione/.

Artificial intelligence and diabetes technology: A review

Artificial intelligence (AI) is widely discussed in the popular literature and is portrayed as impacting many aspects of human life, both in and out of the workplace. The potential for revolutionizing healthcare is significant because of the availability of increasingly powerful computational platforms and methods, along with increasingly informative sources of patient data, both in and out of clinical settings. This review aims to provide a realistic assessment of the potential for AI in understanding and managing diabetes, accounting for the state of the art in the methodology and medical devices that collect data, process data, and act accordingly. Acknowledging that many conflicting definitions of AI have been put forth, this article attempts to characterize the main elements of the field as they relate to diabetes, identifying the main perspectives and methods that can (i) affect basic understanding of the disease, (ii) affect understanding of risk factors (genetic, clinical, and behavioral) of diabetes development, (iii) improve diagnosis, (iv) improve understanding of the arc of disease (progression and personal/societal impact), and finally (v) improve treatment.

Synthetic biomarkers: a twenty-first century path to early cancer detection

Detection of cancer at an early stage when it is still localized improves patient response to medical interventions for most cancer types. The success of screening tools such as cervical cytology to reduce mortality has spurred significant interest in new methods for early detection (for example, using non-invasive blood-based or biofluid-based biomarkers). Yet biomarkers shed from early lesions are limited by fundamental biological and mass transport barriers - such as short circulation times and blood dilution - that limit early detection. To address this issue, synthetic biomarkers are being developed. These represent an emerging class of diagnostics that deploy bioengineered sensors inside the body to query early-stage tumours and amplify disease signals to levels that could potentially exceed those of shed biomarkers. These strategies leverage design principles and advances from chemistry, synthetic biology and cell engineering. In this Review, we discuss the rationale for development of biofluid-based synthetic biomarkers. We examine how these strategies harness dysregulated features of tumours to amplify detection signals, use tumour-selective activation to increase specificity and leverage natural processing of bodily fluids (for example, blood, urine and proximal fluids) for easy detection. Finally, we highlight the challenges that exist for preclinical development and clinical translation of synthetic biomarker diagnostics.

Elafibranor and liraglutide improve differentially liver health and metabolism in a mouse model of non-alcoholic steatohepatitis

BACKGROUND & AIMS

This study aimed to assess and compare the effects of the GLP-1 analog liraglutide and the PPARα/δ agonist elafibranor on liver histology and their impact on hepatic lipidome, metabolome, Kupffer and hepatic stellate cell activation in a model of advanced non-alcoholic fatty liver disease (NAFLD).

METHODS

Male C57BL/6JRj mice with biopsy-confirmed hepatosteatosis and fibrosis induced by 36-week Amylin liver NASH (AMLN) diet (high-fat, fructose and cholesterol) were randomized to receive for 12 weeks: (a) liraglutide (0.4 mg/kg/day s.c.), (b) elafibranor (30 mg/kg/day p.o.) and (c) vehicle. Metabolic status, liver pathology, markers of inflammation, Kupffer and stellate cell activation, and metabolomics/lipidomics were assessed at study completion.

RESULTS

Elafibranor and liraglutide improved weight, insulin sensitivity, glucose homeostasis and NAFLD activity score (pre-to-post biopsy). Elafibranor had a profound effect on hepatic lipidome, demonstrated by reductions in glycerides, increases in phospholipids, and by beneficial regulation of mediators of fatty acid oxidation, inflammation and oxidative stress. Liraglutide had a major impact on inflammatory and fibrogenic markers of Kupffer and hepatic stellate cell activation (Galectin-3, Collagen type I alpha 1, alpha-smooth muscle actin). Liraglutide exerted beneficial effects on bile acid and carbohydrate metabolism, demonstrated by restorations of the concentrations of bile acids, glycogen metabolism by-products and pentoses, thus facilitating glycogen utilization turnover and nucleic acid formation.

CONCLUSIONS

Liraglutide and elafibranor robustly but through different pathways improve overall metabolic health and liver status in NAFLD. These data indicate important differences in the respective mechanisms of action and support the notion for their evaluation as combination therapies in the future.

Empagliflozin Improves Metabolic and Hepatic Outcomes in a Non-Diabetic Obese Biopsy-Proven Mouse Model of Advanced NASH

Empagliflozin, an established treatment for type 2 diabetes (T2DM), has shown beneficial effects on liver steatosis and fibrosis in animals and in humans with T2DM, non-alcoholic fatty liver disease (NAFLD) and steatohepatitis (NASH). However, little is known about the effects of empagliflozin on liver function in advanced NASH with liver fibrosis and without diabetes. This study aimed to assess the effects of empagliflozin on hepatic and metabolic outcomes in a diet-induced obese (DIO) and insulin-resistant but non-diabetic biopsy-confirmed mouse model of advanced NASH. Male C57BL/6JRj mice with a biopsy-confirmed steatosis and fibrosis on AMLN diet (high fat, fructose and cholesterol) for 36-weeks were randomized to receive for 12 weeks: (a) Empagliflozin (10 mg/kg/d p.o.), or (b) vehicle. Metabolic outcomes, liver pathology, markers of Kupffer and stellate cell activation and lipidomics were assessed at the treatment completion. Empagliflozin did not affect the body weight, body composition or insulin sensitivity (assessed by intraperitoneal insulin tolerance test), but significantly improved glucose homeostasis as assessed by oral glucose tolerance test in DIO-NASH mice. Empagliflozin improved modestly the NAFLD activity score compared with the vehicle, mainly by improving inflammation and without affecting steatosis, the fibrosis stage and markers of Kupffer and stellate cell activation. Empagliflozin reduced the hepatic concentrations of pro-inflammatory lactosylceramides and increased the concentrations of anti-inflammatory polyunsaturated triglycerides. Empagliflozin exerts beneficial metabolic and hepatic (mainly anti-inflammatory) effects in non-diabetic DIO-NASH mice and thus may be effective against NASH even in non-diabetic conditions.

Epigenetic landscape in blood leukocytes following ketosis and weight loss induced by a very low calorie ketogenic diet (VLCKD) in patients with obesity

BACKGROUND

The molecular mechanisms underlying the potential health benefits of a ketogenic diet are unknown and could be mediated by epigenetic mechanisms.

OBJECTIVE

To identify the changes in the obesity-related methylome that are mediated by the induced weight loss or are dependent on ketosis in subjects with obesity underwent a very-low calorie ketogenic diet (VLCKD).

METHODS

Twenty-one patients with obesity (n = 12 women, 47.9 ± 1.02 yr, 33.0 ± 0.2 kg/m²) after 6 months on a VLCKD and 12 normal weight volunteers (n = 6 women, 50.3 ± 6.2 yrs, 22.7 ± 1.5 kg/m²) were studied. Data from the Infinium MethylationEPIC BeadChip methylomes of blood leukocytes were obtained at time points of ketotic phases (basal, maximum ketosis, and out of ketosis) during VLCKD (n = 10) and at baseline in volunteers (n = 12). Results were further validated by pyrosequencing in representative cohort of patients on a VLCKD (n = 18) and correlated with gene expression.

RESULTS

After weight reduction by VLCKD, differences were found at 988 CpG sites (786 unique genes). The VLCKD altered methylation levels in patients with obesity had high resemblance with those from normal weight volunteers and was concomitant with a downregulation of DNA methyltransferases (DNMT)1, 3a and 3b. Most of the encoded genes were involved in metabolic processes, protein metabolism, and muscle, organ, and skeletal system development. Novel genes representing the top scoring associated events were identified, including ZNF331, FGFRL1 (VLCKD-induced weight loss) and CBFA2T3, C3orf38, JSRP1, and LRFN4 (VLCKD-induced ketosis). Interestingly, ZNF331 and FGFRL1 were validated in an independent cohort and inversely correlated with gene expression.

CONCLUSIONS

The beneficial effects of VLCKD therapy on obesity involve a methylome more suggestive of normal weight that could be mainly mediated by the VLCKD-induced ketosis rather than weight loss.

Perspectives on Precision Medicine Approaches to NAFLD Diagnosis and Management

Precision medicine defines the attempt to identify the most effective approaches for specific subsets of patients based on their genetic background, clinical features, and environmental factors. Nonalcoholic fatty liver disease (NAFLD) encompasses the alcohol-like spectrum of liver disorders (steatosis, steatohepatitis with/without fibrosis, and cirrhosis and hepatocellular carcinoma) in the nonalcoholic patient. Recently, disease renaming to MAFLD [metabolic (dysfunction)-associated fatty liver disease] and positive criteria for diagnosis have been proposed. This review article is specifically devoted to envisaging some clues that may be useful to implementing a precision medicine-oriented approach in research and clinical practice. To this end, we focus on how sex and reproductive status, genetics, intestinal microbiota diversity, endocrine and metabolic status, as well as physical activity may interact in determining NAFLD/MAFLD heterogeneity. All these factors should be considered in the individual patient with the aim of implementing an individualized therapeutic plan. The impact of considering NAFLD heterogeneity on the development of targeted therapies for NAFLD subgroups is also extensively discussed.

Demetra Application: An integrated genotype analysis web server for clinical genomics in endometriosis

Demetra Application is a holistic integrated and scalable bioinformatics web-based tool designed to assist medical experts and researchers in the process of diagnosing endometriosis. The application identifies the most prominent gene variants and single nucleotide polymorphisms (SNPs) causing endometriosis using the genomic data provided for the patient by a medical expert. The present study analyzed >28.000 endometriosis-related publications using data mining and semantic techniques aimed towards extracting the endometriosis-related genes and SNPs. The extracted knowledge was filtered, evaluated, annotated, classified, and stored in the Demetra Application Database (DAD). Moreover, an updated gene regulatory network with the genes implements in endometriosis was established. This was followed by the design and development of the Demetra Application, in which the generated datasets and results were included. The application was tested and presented herein with whole-exome sequencing data from seven related patients with endometriosis. Endometriosis-related SNPs and variants identified in genome-wide association studies (GWAS), whole-genome (WGS), whole-exome (WES), or targeted sequencing information were classified, annotated and analyzed in a consolidated patient profile with clinical significance information. Probable genes associated with the patient's genomic profile were visualized using several graphs, including chromosome ideograms, statistic bars and regulatory networks through data mining studies with relative publications, in an effort to obtain a representative number of the most credible candidate genes and biological pathways associated with endometriosis. An evaluation analysis was performed on seven patients from a three-generation family with endometriosis. All the recognized gene variants that were previously considered to be associated with endometriosis were properly identified in the output profile per patient, and by comparing the results, novel findings emerged. This novel and accessible webserver tool of endometriosis to assist medical experts in the clinical genomics and precision medicine procedure is available at http://geneticslab.aua.gr/.

Multi-Layer Picture of Neurodegenerative Diseases: Lessons from the Use of Big Data through Artificial Intelligence

In the big data era, artificial intelligence techniques have been applied to tackle traditional issues in the study of neurodegenerative diseases. Despite the progress made in understanding the complex (epi)genetics signatures underlying neurodegenerative disorders, performing early diagnosis and developing drug repurposing strategies remain serious challenges for such conditions. In this context, the integration of multi-omics, neuroimaging, and electronic health records data can be exploited using deep learning methods to provide the most accurate representation of patients possible. Deep learning allows researchers to find multi-modal biomarkers to develop more effective and personalized treatments, early diagnosis tools, as well as useful information for drug discovering and repurposing in neurodegenerative pathologies. In this review, we will describe how relevant studies have been able to demonstrate the potential of deep learning to enhance the knowledge of neurodegenerative disorders such as Alzheimer's and Parkinson's diseases through the integration of all sources of biomedical data.

The Bee Hemolymph Metabolome: A Window into the Impact of Viruses on Bumble Bees

State-of-the-art virus detection technology has advanced a lot, yet technology to evaluate the impacts of viruses on bee physiology and health is basically lacking. However, such technology is sorely needed to understand how multi-host viruses can impact the composition of the bee community. Here, we evaluated the potential of hemolymph metabolites as biomarkers to identify the viral infection status in bees. A metabolomics strategy based on ultra-high-performance liquid chromatography coupled to high-resolution mass spectrometry was implemented. First, we constructed a predictive model for standardized bumble bees, in which non-infected bees were metabolically differentiated from an overt Israeli acute paralysis virus (IAPV) infection (R²Y = 0.993; Q² = 0.906), as well as a covert slow bee paralysis virus (SBPV) infection (R²Y = 0.999; Q² = 0.875). Second, two sets of potential biomarkers were identified, being descriptors for the metabolomic changes in the bee's hemolymph following viral infection. Third, the biomarker sets were evaluated in a new dataset only containing wild bees and successfully discriminated virus infection versus non-virus infection with an AUC of 0.985. We concluded that screening hemolymph metabolite markers can underpin physiological changes linked to virus infection dynamics, opening promising avenues to identify, monitor, and predict the effects of virus infection in a bee community within a specific environment.

Coupling Machine Learning and Lipidomics as a Tool to Investigate Metabolic Dysfunction-Associated Fatty Liver Disease. A General Overview

Hepatic biopsy is the gold standard for staging nonalcoholic fatty liver disease (NAFLD). Unfortunately, accessing the liver is invasive, requires a multidisciplinary team and is too expensive to be conducted on large segments of the population. NAFLD starts quietly and can progress until liver damage is irreversible. Given this complex situation, the search for noninvasive alternatives is clinically important. A hallmark of NAFLD progression is the dysregulation in lipid metabolism. In this context, recent advances in the area of machine learning have increased the interest in evaluating whether multi-omics data analysis performed on peripheral blood can enhance human interpretation. In the present review, we show how the use of machine learning can identify sets of lipids as predictive biomarkers of NAFLD progression. This approach could potentially help clinicians to improve the diagnosis accuracy and predict the future risk of the disease. While NAFLD has no effective treatment yet, the key to slowing the progression of the disease may lie in predictive robust biomarkers. Hence, to detect this disease as soon as possible, the use of computational science can help us to make a more accurate and reliable diagnosis. We aimed to provide a general overview for all readers interested in implementing these methods.

A Detailed Catalogue of Multi-Omics Methodologies for Identification of Putative Biomarkers and Causal Molecular Networks in Translational Cancer Research

Recent advances in sequencing and biotechnological methodologies have led to the generation of large volumes of molecular data of different omics layers, such as genomics, transcriptomics, proteomics and metabolomics. Integration of these data with clinical information provides new opportunities to discover how perturbations in biological processes lead to disease. Using data-driven approaches for the integration and interpretation of multi-omics data could stably identify links between structural and functional information and propose causal molecular networks with potential impact on cancer pathophysiology. This knowledge can then be used to improve disease diagnosis, prognosis, prevention, and therapy. This review will summarize and categorize the most current computational methodologies and tools for integration of distinct molecular layers in the context of translational cancer research and personalized therapy. Additionally, the bioinformatics tools Multi-Omics Factor Analysis (MOFA) and netDX will be tested using omics data from public cancer resources, to assess their overall robustness, provide reproducible workflows for gaining biological knowledge from multi-omics data, and to comprehensively understand the significantly perturbed biological entities in distinct cancer types. We show that the performed supervised and unsupervised analyses result in meaningful and novel findings.