Machine learning meets omics: applications and perspectives

Multi-omics: a bridge connecting genotype and phenotype for epilepsy?

Epilepsy is a collection of neurological disorders characterized by abnormal neuronal discharges, resulting in spontaneous and recurrent unprovoked seizures. Despite the use of over 20 anti-seizure drugs, conventional one-size-fits-all approaches are insufficient to meet the needs of all patients, and about 1/3 patients developed drug-resistant epilepsy. Recently, the establishment of precision medicine-based clinical management for epilepsy may bring new insights, especially omics-based approaches. Single omics approach is limited to addressing questions from a single molecular perspective. Whereas multi-omics approaches enable a comprehensive characterization of multiple molecules, revealing the complex molecular dysregulation networks underlying different epilepsy phenotypes. Furthermore, multi-omics methods have catalyzed a paradigm shift in scientific inquiry, transitioning from traditional hypothesis-driven types to data-driven research architectures. Despite the full potential of multi-omics research yet to be realized, its application in epilepsy holds great promise, from the discovery of epileptic biomarkers to personalized management. In this review, we performed a comprehensive overview of the omics technologies and multi-omics integration strategies, followed by an exploration of their role in enhancing the management of epilepsy treatment and care, hoping to provide new directions for future researches.

Molecular insights fast-tracked: AI in biosynthetic pathway research

Covering: 2000 to 2025This review explores the potential of artificial intelligence (AI) in addressing challenges and accelerating molecular insights in biosynthetic pathway research, which is crucial for developing bioactive natural products with applications in pharmacology, agriculture, and biotechnology. It provides an overview of various AI techniques relevant to this research field, including machine learning (ML), deep learning (DL), natural language processing, network analysis, and data mining. AI-powered applications across three main areas, namely, pathway discovery and mining, pathway design, and pathway optimization, are discussed, and the benefits and challenges of integrating omics data and AI for enhanced pathway research are also elucidated. This review also addresses the current limitations, future directions, and the importance of synergy between AI and experimental approaches in unlocking rapid advancements in biosynthetic pathway research. The review concludes with an evaluation of AI's current capabilities and future outlook, emphasizing the transformative impact of AI on biosynthetic pathway research and the potential for new opportunities in the discovery and optimization of bioactive natural products.

Cancer-on-a-chip for precision cancer medicine

Many cancer therapies fail in clinical trials despite showing potent efficacy in preclinical studies. One of the key reasons is the adopted preclinical models cannot recapitulate the complex tumor microenvironment (TME) and reflect the heterogeneity and patient specificity in human cancer. Cancer-on-a-chip (CoC) microphysiological systems can closely mimic the complex anatomical features and microenvironment interactions in an actual tumor, enabling more accurate disease modeling and therapy testing. This review article concisely summarizes and highlights the state-of-the-art progresses in CoC development for modeling critical TME compartments including the tumor vasculature, stromal and immune niche, as well as its applications in therapying screening. Current dilemma in cancer therapy development demonstrates that future preclinical models should reflect patient specific pathophysiology and heterogeneity with high accuracy and enable high-throughput screening for anticancer drug discovery and development. Therefore, CoC should be evolved as well. We explore future directions and discuss the pathway to develop the next generation of CoC models for precision cancer medicine, such as patient-derived chip, organoids-on-a-chip, and multi-organs-on-a-chip with high fidelity. We also discuss how the integration of sensors and microenvironmental control modules can provide a more comprehensive investigation of disease mechanisms and therapies. Next, we outline the roadmap of future standardization and translation of CoC technology toward real-world applications in pharmaceutical development and clinical settings for precision cancer medicine and the practical challenges and ethical concerns. Finally, we overview how applying advanced artificial intelligence tools and computational models could exploit CoC-derived data and augment the analytical ability of CoC.

Integrative bioinformatics analysis and experimental validation of key biomarkers driving the progression of cirrhotic portal hypertension

Background

Portal hypertension is a driving factor of cirrhosis complications, but the specific molecular mechanism of portal hypertension in cirrhosis remains unclear. The aim of this study was to identify hub genes for predicting persistent progression of portal hypertension in patients with liver cirrhosis.

Methods

Related microarray datasets were obtained from the Gene Expression Omnibus database. Weighted gene co-expression network analysis and differential expression genes analysis were used to identify the correlation sets of genes. In addition, protein-protein interaction networks and machine learning algorithms were conducted to screen center of candidate genes. To validate the diagnostic effect of hub genes, receiver operating characteristic curves were utilized in another dataset that is publicly accessible. Furthermore, the CIBERSORT algorithm was employed to investigate the immune infiltration levels of 22 immune cells and their connection to hub gene markers. Immunohistochemistry and reverse transcription quantitative polymerase chain reaction (RT-qPCR) were conducted to validate novel hub genes in clinical specimens.

Results

We obtained 671 differentially expressed genes and 11 module genes related to cirrhotic portal hypertension. Two candidate genes namely oncoprotein-induced transcript 3 protein (OIT3) and lysyl oxidase like protein 1 (LOXL1) were identified as biomarkers. RT-qPCR and immunohistochemistry (IHC) verified the expression of LOXL1 and OIT3 at mRNA and protein levels in liver tissue.

Conclusions

OIT3 and LOXL1 were identified as potential novel targets for the diagnosis and treatment of cirrhotic portal hypertension (CPH).

Associations between plasma proteomic signatures and secondary sleep in older adults

Sleep disturbances are prevalent among elderly populations and are linked to various health complications. Understanding the underlying biological mechanisms contributing to sleep disorders is crucial for developing targeted interventions. In this study, we measured 355 plasma proteins in an elderly Japanese cohort (n=77) using a high-throughput proteomic platform. Additionally, we collected over 25,000 person-days of physical activity and sleep behavior data from wrist-worn wearable devices, focusing on total sleep time (TST) across 24 h and daytime sleep. Fragmented sleep was observed as one of the most prevalent sleep disturbances in this population. In protein expression analysis, we identified 9 protein biomarkers associated with increased secondary sleep TST, defined as additional sleep episodes outside of the main sleep episode within 24 h. These findings may suggest disruptions in circadian rhythms or underlying health conditions. Functional analysis revealed that biological processes related to inflammation play a significant role in regulating sleep behavior. Further analysis showed an association of 12 proteins with daytime sleep and 5 proteins with afternoon sleep. Overall, this study identified inflammatory biomarkers and biological processes associated with sleep behavior in the elderly, presenting promising opportunities for developing diagnostic tools and targeted clinical interventions.

Omics research in atherosclerosis

Atherosclerosis (AS) is a chronic inflammatory disease characterized by lipid deposition within the arterial intima, as well as fibrous tissue proliferation and calcification. AS has long been recognized as one of the primary pathological foundations of cardiovascular diseases in humans. Its pathogenesis is intricate and not yet fully elucidated. Studies have shown that AS is associated with oxidative stress, inflammatory response, lipid deposition, and changes in cell phenotype. Unfortunately, there is currently no effective prevention or targeted treatment for AS. The rapid advancement of omics technologies, including genomics, transcriptomics, proteomics, and metabolomics, has opened up novel avenues to elucidate the fundamental pathophysiology and associated mechanisms of AS. Here, we review articles published over the past decade and focus on the current status, challenges, limitations, and prospects of omics in AS research and clinical practice. Emphasizing potential targets based on omics technologies will improve our understanding of this pathological condition and assist in the development of potential therapeutic approaches for AS-related diseases.

Genome language modeling (GLM): a beginner's cheat sheet

Integrating genomics with diverse data modalities has the potential to revolutionize personalized medicine. However, this integration poses significant challenges due to the fundamental differences in data types and structures. The vast size of the genome necessitates transformation into a condensed representation containing key biomarkers and relevant features to ensure interoperability with other modalities. This commentary explores both conventional and state-of-the-art approaches to genome language modeling (GLM), with a focus on representing and extracting meaningful features from genomic sequences. We focus on the latest trends of applying language modeling techniques on genomics sequence data, treating it as a text modality. Effective feature extraction is essential in enabling machine learning models to effectively analyze large genomic datasets, particularly within multimodal frameworks. We first provide a step-by-step guide to various genomic sequence preprocessing and tokenization techniques. Then we explore feature extraction methods for the transformation of tokens using frequency, embedding, and neural network-based approaches. In the end, we discuss machine learning (ML) applications in genomics, focusing on classification, regression, language processing algorithms, and multimodal integration. Additionally, we explore the role of GLM in functional annotation, emphasizing how advanced ML models, such as Bidirectional encoder representations from transformers, enhance the interpretation of genomic data. To the best of our knowledge, we compile the first end-to-end analytic guide to convert complex genomic data into biologically interpretable information using GLM, thereby facilitating the development of novel data-driven hypotheses.

Diagnosis of parotid gland tumors using a ternary classification model based on ultrasound radiomics

Objective

This study aimed to evaluate the diagnostic value of two-step ultrasound radiomics models in distinguishing parotid malignancies from pleomorphic adenomas (PAs) and Warthin's tumors (WTs).

Methods

A retrospective analysis was conducted on patients who underwent parotidectomy at our institution between January 2015 and December 2022. Radiomics features were extracted from two-dimensional (2D) ultrasound images using 3D Slicer. Feature selection was performed using the Mann-Whitney U test and seven additional selection methods. Two-step LASSO-BNB and voting ensemble learning modeling algorithm with recursive feature elimination feature selection method (RFE-Voting) models were then applied for classification. Model performance was assessed using the area under the receiver operating characteristic curve (AUC), and internal validation was conducted through fivefold cross-validation.

Results

A total of 336 patients were included in the study, comprising 73 with malignant tumors and 263 with benign lesions (118 WT and 145 PA). The LASSO-NB model demonstrated excellent performance in distinguishing between benign and malignant parotid lesions, achieving an AUC of 0.910 (95% CI, 0.907-0.914), with an accuracy of 86.8%, sensitivity of 92.5%, and specificity of 66.7%, significantly outperforming experienced sonographers (accuracy of 61.90%). The RFE-Voting model also showed outstanding performance in differentiating PA from WT, with an AUC of 0.962 (95% CI, 0.959-0.963), accuracy of 83.0%, sensitivity of 84.0%, and specificity of 92.1%, exceeding the diagnostic capability of experienced sonographers (accuracy of 65.39%).

Conclusion

The two-step LASSO-BNB and RFE-Voting models based on ultrasound imaging performed well in distinguishing glandular malignant tumors from PA and WT and have good predictive capabilities, which can provide more useful information for non-invasive differentiation of parotid gland tumors before surgery.

Integrated Machine Learning Algorithms-Enhanced Predication for Cervical Cancer from Mass Spectrometry-Based Proteomics Data

Early diagnosis is critical for improving outcomes in cancer patients; however, the application of diagnostic markers derived from serum proteomic screening remains challenging. Artificial intelligence (AI), encompassing deep learning and machine learning (ML), has gained increasing prominence across various scientific disciplines. In this study, we utilized cervical cancer (CC) as a model to develop an AI-driven pipeline for the identification and validation of serum biomarkers for early cancer diagnosis, leveraging mass spectrometry-based proteomics data. By processing and normalizing serum polypeptide differential peaks from 240 patients, we employed eight distinct ML algorithms to classify and analyze these differential polypeptide peaks, subsequently constructing receiver operating characteristic (ROC) curves and confusion matrices. Key performance metrics, including accuracy, precision, recall, and F1 score, were systematically evaluated. Furthermore, by integrating feature importance values, Shapley values, and local interpretable model-agnostic explanation (LIME) values, we demonstrated that the diagnostic area under the curve (AUC) achieved by our multi-dimensional learning models approached 1, significantly outperforming the diagnostic AUC of single markers derived from the PRIDE database. These findings underscore the potential of proteomics-driven integrated machine learning as a robust strategy to enhance early cancer diagnosis, offering a promising avenue for clinical translation.

Inferring kinase-phosphosite regulation from phosphoproteome-enriched cancer multi-omics datasets

Phosphorylation in eukaryotic cells plays a key role in regulating cell signaling and disease progression. Despite the ability to detect thousands of phosphosites in a single experiment using high-throughput technologies, the kinases responsible for regulating these sites are largely unidentified. To solve this, we collected the quantitative data at the transcriptional, protein, and phosphorylation levels of 10 159 samples from 23 tumor datasets and 15 adjacent normal tissue datasets. Our analysis aimed to uncover the potential impact and linkage of kinase-phosphosite (KPS) pairs through experimental evidence in publications and prediction tools commonly used. We discovered that both experimentally validated and tool-predicted KPS pairs were enriched in groups where there is a significant correlation between kinase expression/phosphorylation level and the phosphorylation level of phosphosite. This suggested that a quantitative correlation could infer the KPS interconnections. Furthermore, the Spearman's correlation coefficient for these pairs were notably higher in tumor samples, indicating that these regulatory interactions are particularly pronounced in tumors. Consequently, building on the KPS correlations of different datasets as predictive features, we have developed an innovative approach that employed an oversampling method combined with and XGBoost algorithm (SMOTE-XGBoost) to predict potential kinase-specific phosphorylation sites in proteins. Moreover, the computed correlations and predictions of kinase-phosphosite interconnections were integrated into the eKPI database (https://ekpi.omicsbio.info/). In summary, our study could provide helpful information and facilitate further research on the regulatory relationship between kinases and phosphosites.

Omics in acute-on-chronic liver failure

Acute-on-chronic liver failure (ACLF) is a critical syndrome that develops in patients with chronic liver disease and is characterized by acute decompensation, single- or multiple-organ failure and high short-term mortality. Over the past few decades, ACLF has been progressively recognized as an independent clinical entity, and several criteria and prognostic scores have been proposed and validated by different scientific societies. However, controversies still exist in some aspects across regions, which mainly involve whether the definition of underlying liver diseases should include cirrhosis and non-cirrhosis. The pathophysiology of ACLF is complicated and remains unclear, although accumulating evidence based on different aetiologies of ACLF shows that it is closely associated with intense systemic inflammation and immune-metabolism disorder, which result in mitochondrial dysfunction and microenvironment imbalance, leading to disease development and organ failure. In-depth insight into the biological pathways involved in the mechanisms of ACLF and potential mechanistic targets that improve patient survival still needs to be investigated. Omics-based analytical techniques, including genomics, transcriptomics, proteomics, metabolomics and microbiomes, have developed rapidly and can offer novel insights into the essential pathophysiologic process of ACLF. In this paper, we briefly reviewed and summarized the current knowledge and recent advances in the definitions, criteria and prognostic assessments of ACLF; we also described the omics techniques and how omics-based analyses have been applied to investigate and characterize the biological mechanisms of ACLF and identify potential predictive biomarkers and therapeutic targets for ACLF. We also outline the challenges, future directions and limitations presented by omics-based analyses in clinical ACLF research.

Artificial Intelligence Applications in Cardio-Oncology: A Comprehensive Review

PURPOSE OF REVIEW

This review explores the role of artificial intelligence (AI) in cardio-oncology, focusing on its latest application across problems in diagnosis, prognosis, risk stratification, and management of cardiovascular (CV) complications in cancer patients. It also highlights multi-omics analysis, explainable AI, and real-time decision-making, while addressing challenges like data heterogeneity and ethical concerns.

RECENT FINDINGS

AI can advance cardio-oncology by leveraging imaging, electronic health records (EHRs), electrocardiograms (ECG), and multi-omics data for early cardiotoxicity detection, stratification and long-term risk prediction. Novel AI-ECG models and imaging techniques improve diagnostic accuracy, while multi-omics analysis identifies biomarkers for personalized treatment. However, significant barriers, including data heterogeneity, lack of transparency, and regulatory challenges, hinder widespread adoption. AI significantly enhances early detection and intervention in cardio-oncology. Future efforts should address the impact of AI technologies on clinical outcomes, and ethical challenges, to enable broader clinical adoption and improve patient care.

Bioinformatics combining machine learning and single-cell sequencing analysis to identify common mechanisms and biomarkers of rheumatoid arthritis and ischemic heart failure

Patients with rheumatoid arthritis (RA) have an increased risk of ischemic heart failure (IHF), but the shared mechanisms are unclear. This study analyzed RNA sequencing data from five RA and IHF datasets to identify common biological mechanisms and significant biomarkers. One hundred and seventy-seven common differentially expressed genes (CDEGs) were identified, with enrichment analysis highlighting pathways related to sarcomere organization, ventricular myocardial tissue morphogenesis, chondrocyte differentiation, prolactin signaling, hematopoietic cell lineage, and protein methyltransferases. Five hub genes (CD2, CD3D, CCL5, IL7R, and SPATA18) were identified through protein-protein interaction (PPI) network analysis and machine learning. Co-expression and immune cell infiltration analyses underscored the importance of the inflammatory immune response, with hub genes showing significant correlations with plasma cells, activated CD4+ T memory cells, monocytes, and T regulatory cells. Single-cell RNA sequencing (scRNA-seq) confirmed hub gene expression primarily in T cells, activated T cells, monocytes, and NK cells. The findings underscore the critical roles of sarcomere organization, prolactin signaling, protein methyltransferase activity, and immune responses in the progression of IHF in RA patients. These insights not only identify valuable biomarkers and therapeutic targets but also offer promising directions for early diagnosis, personalized treatments, and preventive strategies for IHF in the context of RA. Moreover, the results highlight opportunities for repurposing existing drugs and developing new therapeutic interventions, which could reduce the risk of IHF in RA patients and improve their overall prognosis.

Development and Validation of a Diagnostic Model for Stanford Type B Aortic Dissection Based on Proteomic Profiling

Purpose

Stanford Type B Aortic Dissection (TBAD), a critical aortic disease, has exhibited stable mortality rates over the past decade. However, diagnostic approaches for TBAD during routine health check-ups are currently lacking. This study focused on developing a model to improve the diagnosis in a population.

Patients and Methods

Serum biomarkers were investigated in 88 participants using proteomic profiling combined with machine learning. The findings were validated using ELISA in other 80 participants. Subsequently, a diagnostic model for TBAD integrating biomarkers with clinical indicators was developed and assessed using machine learning.

Results

Six differentially expressed proteins (DEPs) were identified through proteomic profiling and machine learning in discovery and derivation cohorts. Five of these (GDF-15, IL6, CD58, LY9, and Siglec-7) were further verified through ELISA validation within the validation cohort. In addition, ten blood-related indicators were selected as clinical indicators. Combining biomarkers and clinical indicators, the machine learning-based models performed well (AUC of the biomarker model = 0.865, AUC of the clinical model = 0.904, and AUC of the combined model = 0.909) using relative quantitation. The performance of the three models was verified (AUC of biomarker model = 0.866, AUC of clinical model = 0.868, and AUC of combined model = 0.886) using absolute quantitation. Crucially, the combined models outperformed individual biomarkers and clinical models, demonstrating superior efficacy.

Conclusion

Using proteomic profiling, we identified serum IL-6, GDF-15, CD58, LY9, and Siglec-7 as TBAD biomarkers. The machine-learning-based diagnostic model exhibited significant potential for TBAD diagnosis using only blood samples within the population.

Developing and validating a drug recommendation system based on tumor microenvironment and drug fingerprint

Introduction

Tumor heterogeneity significantly complicates the selection of effective cancer treatments, as patient responses to drugs can vary widely. Personalized cancer therapy has emerged as a promising strategy to enhance treatment effectiveness and precision. This study aimed to develop a personalized drug recommendation model leveraging genomic profiles to optimize therapeutic outcomes.

Methods

A content-based filtering algorithm was implemented to predict drug sensitivity. Patient features were characterized by the tumor microenvironment (TME), and drug features were represented by drug fingerprints. The model was trained and validated using the Genomics of Drug Sensitivity in Cancer (GDSC) database, followed by independent validation with the Cancer Cell Line Encyclopedia (CCLE) dataset. Clinical application was assessed using The Cancer Genome Atlas (TCGA) dataset, with Best Overall Response (BOR) serving as the clinical efficacy measure. Two multilayer perceptron (MLP) models were built to predict IC50 values for 542 tumor cell lines across 18 drugs.

Results

The model exhibited high predictive accuracy, with correlation coefficients (R) of 0.914 in the training set and 0.902 in the test set. Predictions for cytotoxic drugs, including Docetaxel (R = 0.72) and Cisplatin (R = 0.71), were particularly robust, whereas predictions for targeted therapies were less accurate (R < 0.3). Validation with CCLE (MFI as the endpoint) showed strong correlations (R = 0.67). Application to TCGA data successfully predicted clinical outcomes, including a significant association with 6-month progression-free survival (PFS, P = 0.007, AUC = 0.793).

Discussion

The model demonstrates strong performance across preclinical datasets, showing its potential for real-world application in personalized cancer therapy. By bridging preclinical IC50 and clinical BOR endpoints, this approach provides a promising tool for optimizing patient-specific treatments.

Artificial intelligence powers regenerative medicine into predictive realm

The expanding regenerative medicine toolkit is reaching a record number of lives. There is a pressing need to enhance the precision, efficiency, and effectiveness of regenerative approaches and achieve reliable outcomes. While regenerative medicine has relied on an empiric paradigm, availability of big data along with advances in informatics and artificial intelligence offer the opportunity to inform the next generation of regenerative sciences along the discovery, translation, and application pathway. Artificial intelligence can streamline discovery and development of optimized biotherapeutics by aiding in the interpretation of readouts associated with optimal repair outcomes. In advanced biomanufacturing, artificial intelligence holds potential in ensuring quality control and assuring scalability through automated monitoring of process-critical variables mandatory for product consistency. In practice application, artificial intelligence can guide clinical trial design, patient selection, delivery strategies, and outcome assessment. As artificial intelligence transforms the regenerative horizon, caution is necessary to reduce bias, ensure generalizability, and mitigate ethical concerns with the goal of equitable access for patients and populations.

Large language models and their applications in bioinformatics

Recent advancements in Natural Language Processing (NLP) have been significantly driven by the development of Large Language Models (LLMs), representing a substantial leap in language-based technology capabilities. These models, built on sophisticated deep learning architectures, typically transformers, are characterized by billions of parameters and extensive training data, enabling them to achieve high accuracy across various tasks. The transformer architecture of LLMs allows them to effectively handle context and sequential information, which is crucial for understanding and generating human language. Beyond traditional NLP applications, LLMs have shown significant promise in bioinformatics, transforming the field by addressing challenges associated with large and complex biological datasets. In genomics, proteomics, and personalized medicine, LLMs facilitate identifying patterns, predicting protein structures, or understanding genetic variations. This capability is crucial, e.g., for advancing drug discovery, where accurate prediction of molecular interactions is essential. This review discusses the current trends in LLMs research and their potential to revolutionize the field of bioinformatics and accelerate novel discoveries in the life sciences.

ESM-BBB-Pred: a fine-tuned ESM 2.0 and deep neural networks for the identification of blood-brain barrier peptides

Blood-brain barrier peptides (BBBP) could significantly improve the delivery of drugs to the brain, paving the way for new treatments for central nervous system (CNS) disorders. The primary challenge in treating CNS disorders lies in the difficulty pharmaceutical agent's face in crossing the BBB. Almost 98% of small molecule drugs and nearly all large molecule drugs fail to penetrate the BBB effectively. Thus, identifying these peptides is vital for advancements in healthcare. This study introduces an enhanced intelligent computational model called BBB-PEP- Evolutionary Scale Modeling (ESM), designed to identify BBBP. The relative positions, reverse position and statistical moment-based features have been utilized on the existing benchmark dataset. For classification purpose, six deep classifiers such as fully connected networks, convolutional neural network, simple recurrent neural networks, long short-term memory (LSTM), bidirectional LSTM, and gated recurrent unit have been utilized. In addition to harnessing the effectiveness of the pre-trained model, a protein language model ESM 2.0 has been fine-tuned on a benchmark dataset for BBBP classification. Three tests such as self-consistency, independent set testing, and five-fold cross-validation have been utilized for evaluation purposes with evaluation metrics includes accuracy, specificity, sensitivity, and Matthews correlation coefficient. The fine-tuned model ESM 2.0 has shown superior results as compared to employed classifiers and surpasses the existing benchmark studies. This system will support future research and the scientific community in the computational identification of BBBP.

A deep learning framework for hepatocellular carcinoma diagnosis using MS1 data

Clinical proteomics analysis is of great significance for analyzing pathological mechanisms and discovering disease-related biomarkers. Using computational methods to accurately predict disease types can effectively improve patient disease diagnosis and prognosis. However, how to eliminate the errors introduced by peptide precursor identification and protein identification for pathological diagnosis remains a major unresolved issue. Here, we develop a powerful end-to-end deep learning model, termed "MS1Former", that is able to classify hepatocellular carcinoma tumors and adjacent non-tumor (normal) tissues directly using raw MS1 spectra without peptide precursor identification. Our model provides accurate discrimination of subtle m/z differences in MS1 between tumor and adjacent non-tumor tissue, as well as more general performance predictions for data-dependent acquisition, data-independent acquisition, and full-scan data. Our model achieves the best performance on multiple external validation datasets. Additionally, we perform a detailed exploration of the model's interpretability. Prospectively, we expect that the advanced end-to-end framework will be more applicable to the classification of other tumors.

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.

An updated overview of the search for biomarkers of osteoporosis based on human proteomics

Osteoporosis is a chronic metabolic disease that increases bone fragility and, leads to severe osteoporotic fractures. In recent years, the use of high-throughput omics to explore physiological and pathological biomarkers related to bone metabolism has gained popularity. In this review, we first briefly review the technical approaches of proteomics. Additionally, we summarize the relevant literature in the last decade to provide a comprehensive overview of advances in human proteomics related to osteoporosis. We describe the specific roles of various proteins related to human bone metabolism, highlighting their potential as biomarkers for risk assessment, early diagnosis and disease course monitoring in osteoporosis. Finally, we outline the main challenges currently faced by human proteomics in the field of osteoporosis and offer suggestions to address these challenges, to inspire the search for novel osteoporosis biomarkers and a foundation for their clinical translation. In conclusion, proteomics is a powerful tool for discovering osteoporosis-related biomarkers, which can not only provide risk assessment, early diagnosis and disease course monitoring, but also reveal the underlying mechanisms of disease and provide key information for personalized treatment.

The translational potential of this article

This review provides an insightful summary of recent human-based studies on osteoporosis-associated proteomics, which can aid the search for novel osteoporosis biomarkers based on human proteomics and the clinical translation of research results.

Integrating Molecular Perspectives: Strategies for Comprehensive Multi-Omics Integrative Data Analysis and Machine Learning Applications in Transcriptomics, Proteomics, and Metabolomics

With the advent of high-throughput technologies, the field of omics has made significant strides in characterizing biological systems at various levels of complexity. Transcriptomics, proteomics, and metabolomics are the three most widely used omics technologies, each providing unique insights into different layers of a biological system. However, analyzing each omics data set separately may not provide a comprehensive understanding of the subject under study. Therefore, integrating multi-omics data has become increasingly important in bioinformatics research. In this article, we review strategies for integrating transcriptomics, proteomics, and metabolomics data, including co-expression analysis, metabolite-gene networks, constraint-based models, pathway enrichment analysis, and interactome analysis. We discuss combined omics integration approaches, correlation-based strategies, and machine learning techniques that utilize one or more types of omics data. By presenting these methods, we aim to provide researchers with a better understanding of how to integrate omics data to gain a more comprehensive view of a biological system, facilitating the identification of complex patterns and interactions that might be missed by single-omics analyses.

From Genomics to Metabolomics: Molecular Insights into Osteoporosis for Enhanced Diagnostic and Therapeutic Approaches

Osteoporosis (OP) is a prevalent skeletal disorder characterized by decreased bone mineral density (BMD) and increased fracture risk. The advancements in omics technologies-genomics, transcriptomics, proteomics, and metabolomics-have provided significant insights into the molecular mechanisms driving OP. These technologies offer critical perspectives on genetic predispositions, gene expression regulation, protein signatures, and metabolic alterations, enabling the identification of novel biomarkers for diagnosis and therapeutic targets. This review underscores the potential of these multi-omics approaches to bridge the gap between basic research and clinical applications, paving the way for precision medicine in OP management. By integrating these technologies, researchers can contribute to improved diagnostics, preventative strategies, and treatments for patients suffering from OP and related conditions.

Screening of genes co-associated with osteoporosis and chronic HBV infection based on bioinformatics analysis and machine learning

Objective

To identify HBV-related genes (HRGs) implicated in osteoporosis (OP) pathogenesis and develop a diagnostic model for early OP detection in chronic HBV infection (CBI) patients.

Methods

Five public sequencing datasets were collected from the GEO database. Gene differential expression and LASSO analyses identified genes linked to OP and CBI. Machine learning algorithms (random forests, support vector machines, and gradient boosting machines) further filtered these genes. The best diagnostic model was chosen based on accuracy and Kappa values. A nomogram model based on HRGs was constructed and assessed for reliability. OP patients were divided into two chronic HBV-related clusters using non-negative matrix factorization. Differential gene expression analysis, Gene Ontology, and KEGG enrichment analyses explored the roles of these genes in OP progression, using ssGSEA and GSVA. Differences in immune cell infiltration between clusters and the correlation between HRGs and immune cells were examined using ssGSEA and the Pearson method.

Results

Differential gene expression analysis of CBI and combined OP dataset identified 822 and 776 differentially expressed genes, respectively, with 43 genes intersecting. Following LASSO analysis and various machine learning recursive feature elimination algorithms, 16 HRGs were identified. The support vector machine emerged as the best predictive model based on accuracy and Kappa values, with AUC values of 0.92, 0.83, 0.74, and 0.7 for the training set, validation set, GSE7429, and GSE7158, respectively. The nomogram model exhibited AUC values of 0.91, 0.79, and 0.68 in the training set, GSE7429, and GSE7158, respectively. Non-negative matrix factorization divided OP patients into two clusters, revealing statistically significant differences in 11 types of immune cell infiltration between clusters. Finally, intersecting the HRGs obtained from LASSO analysis with the HRGs identified three genes.

Conclusion

This study successfully identified HRGs and developed an efficient diagnostic model based on HRGs, demonstrating high accuracy and strong predictive performance across multiple datasets. This research not only offers new insights into the complex relationship between OP and CBI but also establishes a foundation for the development of early diagnostic and personalized treatment strategies for chronic HBV-related OP.

Advancing microbial production through artificial intelligence-aided biology

Microbial cell factories (MCFs) have been leveraged to construct sustainable platforms for value-added compound production. To optimize metabolism and reach optimal productivity, synthetic biology has developed various genetic devices to engineer microbial systems by gene editing, high-throughput protein engineering, and dynamic regulation. However, current synthetic biology methodologies still rely heavily on manual design, laborious testing, and exhaustive analysis. The emerging interdisciplinary field of artificial intelligence (AI) and biology has become pivotal in addressing the remaining challenges. AI-aided microbial production harnesses the power of processing, learning, and predicting vast amounts of biological data within seconds, providing outputs with high probability. With well-trained AI models, the conventional Design-Build-Test (DBT) cycle has been transformed into a multidimensional Design-Build-Test-Learn-Predict (DBTLP) workflow, leading to significantly improved operational efficiency and reduced labor consumption. Here, we comprehensively review the main components and recent advances in AI-aided microbial production, focusing on genome annotation, AI-aided protein engineering, artificial functional protein design, and AI-enabled pathway prediction. Finally, we discuss the challenges of integrating novel AI techniques into biology and propose the potential of large language models (LLMs) in advancing microbial production.

The impact and future of artificial intelligence in medical genetics and molecular medicine: an ongoing revolution

Artificial intelligence (AI) platforms have emerged as pivotal tools in genetics and molecular medicine, as in many other fields. The growth in patient data, identification of new diseases and phenotypes, discovery of new intracellular pathways, availability of greater sets of omics data, and the need to continuously analyse them have led to the development of new AI platforms. AI continues to weave its way into the fabric of genetics with the potential to unlock new discoveries and enhance patient care. This technology is setting the stage for breakthroughs across various domains, including dysmorphology, rare hereditary diseases, cancers, clinical microbiomics, the investigation of zoonotic diseases, omics studies in all medical disciplines. AI's role in facilitating a deeper understanding of these areas heralds a new era of personalised medicine, where treatments and diagnoses are tailored to the individual's molecular features, offering a more precise approach to combating genetic or acquired disorders. The significance of these AI platforms is growing as they assist healthcare professionals in the diagnostic and treatment processes, marking a pivotal shift towards more informed, efficient, and effective medical practice. In this review, we will explore the range of AI tools available and show how they have become vital in various sectors of genomic research supporting clinical decisions.

Identification and experimental verification of immune-related hub genes in intervertebral disc degeneration

Background

Inflammation and immune factors are the core of intervertebral disc degeneration (IDD), but the immune environment and epigenetic regulation process of IDD remain unclear. This study aims to identify immune-related diagnostic candidate genes for IDD, and search for potential pathogenesis and therapeutic targets for IDD.

Methods

Gene expression datasets were obtained from the Gene Expression Omnibus (GEO). Differential expression immune genes (Imm-DEGs) were identified through weighted gene correlation network analysis (WGCNA) and linear models for microarray data analysis (Limma). LASSO algorithm was used to identify feature genes related to IDD, which were compared with core node genes in PPI network to obtain hub genes. Based on the coefficients of hub genes, a risk model was constructed, and the diagnostic value of hub genes was further evaluated through receiver operating characteristic (ROC) analysis. Xcell, an immunocyte analysis tool, was used to estimate the infiltration of immune cells. Finally, nucleus pulposus cells were co-cultured with macrophages to create an M1 macrophage immune inflammatory environment, and the changes of hub genes were verified.

Results

Combined with the results of WGCNA and Limma gene differential analysis, a total of 30 Imm-DEGs were identified. Imm-DEGs enriched in multiple pathways related to immunity and inflammation. LASSO algorithm identified 10 feature genes from Imm-DEGs that significantly affected IDD, and after comparison with core node genes in the PPI network of Imm-DEGs, 6 hub genes (NR1H3, SORT1, PTGDS, AGT, IRF1, TGFB2) were determined. Results of ROC curves and external dataset validation showed that the risk model constructed with the 6 hub genes had high diagnostic value for IDD. Immunocyte infiltration analysis showed the presence of various dysregulated immune cells in the degenerative nucleus pulposus tissue. In vitro experimental results showed that the gene expression of NR1H3, SORT1, PTGDS, IRF1, and TGFB2 in nucleus pulposus cells in the immune inflammatory environment was up-regulated, but the change of AGT was not significant.

Conclusions

The hub genes NR1H3, SORT1, PTGDS, IRF1, and TGFB2 can be used as immunorelated biomarkers for IDD, and may be potential targets for immune regulation therapy for IDD.

A novel integrated extraction protocol for multi-omic studies in heavily degraded samples

The combination of multi-omic techniques, such as genomics, transcriptomics, proteomics, metabolomics and epigenomics, has revolutionised studies in medical research. These techniques are employed to support biomarker discovery, better understand molecular pathways and identify novel drug targets. Despite concerted efforts in integrating omic datasets, there is an absence of protocols that integrate all four biomolecules in a single extraction process. Here, we demonstrate for the first time a minimally destructive integrated protocol for the simultaneous extraction of artificially degraded DNA, proteins, lipids and metabolites from pig brain samples. We used an MTBE-based approach to separate lipids and metabolites, followed by subsequent isolation of DNA and proteins. We have validated this protocol against standalone extraction protocols and show comparable or higher yields of all four biomolecules. This integrated protocol is key to facilitating the preservation of irreplaceable samples while promoting downstream analyses and successful data integration by removing bias from univariate dataset noise and varied distribution characteristics.

From Lipid Signatures to Cellular Responses: Unraveling the Complexity of Melanoma and Furthering Its Diagnosis and Treatment

Recent advancements in mass spectrometry have significantly enhanced our understanding of complex lipid profiles, opening new avenues for oncological diagnostics. This review highlights the importance of lipidomics in the comprehension of certain metabolic pathways and its potential for the detection and characterization of various cancers, in particular melanoma. Through detailed case studies, we demonstrate how lipidomic analysis has led to significant breakthroughs in the identification and understanding of cancer types and its potential for detecting unique biomarkers that are instrumental in its diagnosis. Additionally, this review addresses the technical challenges and future perspectives of these methodologies, including their potential expansion and refinement for clinical applications. The discussion underscores the critical role of lipidomic profiling in advancing cancer diagnostics, proposing a new paradigm in how we approach this devastating disease, with particular emphasis on its application in comparative oncology.

GeneRaMeN enables integration, comparison, and meta-analysis of multiple ranked gene lists to identify consensus, unique, and correlated genes

High-throughput experiments often produce ranked gene outputs, with forward genetic screening being a notable example. While there are various tools for analyzing individual datasets, those that perform comparative and meta-analytical examination of such ranked gene lists remain scarce. Here, we introduce Gene Rank Meta Analyzer (GeneRaMeN), an R Shiny tool utilizing rank statistics to facilitate the identification of consensus, unique, and correlated genes across multiple hit lists. We focused on two key topics to showcase GeneRaMeN: virus host factors and cancer dependencies. Using GeneRaMeN 'Rank Aggregation', we integrated 24 published and new flavivirus genetic screening datasets, including dengue, Japanese encephalitis, and Zika viruses. This meta-analysis yielded a consensus list of flavivirus host factors, elucidating the significant influence of cell line selection on screening outcomes. Similar analysis on 13 SARS-CoV-2 CRISPR screening datasets highlighted the pivotal role of meta-analysis in revealing redundant biological pathways exploited by the virus to enter human cells. Such redundancy was further underscored using GeneRaMeN's 'Rank Correlation', where a strong negative correlation was observed for host factors implicated in one entry pathway versus the alternate route. Utilizing GeneRaMeN's 'Rank Uniqueness', we analyzed human coronaviruses 229E, OC43, and SARS-CoV-2 datasets, identifying host factors uniquely associated with a defined subset of the screening datasets. Similar analyses were performed on over 1000 Cancer Dependency Map (DepMap) datasets spanning 19 human cancer types to reveal unique cancer vulnerabilities for each organ/tissue. GeneRaMeN, an efficient tool to integrate and maximize the usability of genetic screening datasets, is freely accessible via https://ysolab.shinyapps.io/GeneRaMeN.

Integrated multi-omics with machine learning to uncover the intricacies of kidney disease

The development of omics technologies has driven a profound expansion in the scale of biological data and the increased complexity in internal dimensions, prompting the utilization of machine learning (ML) as a powerful toolkit for extracting knowledge and understanding underlying biological patterns. Kidney disease represents one of the major growing global health threats with intricate pathogenic mechanisms and a lack of precise molecular pathology-based therapeutic modalities. Accordingly, there is a need for advanced high-throughput approaches to capture implicit molecular features and complement current experiments and statistics. This review aims to delineate strategies for integrating multi-omics data with appropriate ML methods, highlighting key clinical translational scenarios, including predicting disease progression risks to improve medical decision-making, comprehensively understanding disease molecular mechanisms, and practical applications of image recognition in renal digital pathology. Examining the benefits and challenges of current integration efforts is expected to shed light on the complexity of kidney disease and advance clinical practice.

Navigating Challenges and Opportunities in Multi-Omics Integration for Personalized Healthcare

The field of multi-omics has witnessed unprecedented growth, converging multiple scientific disciplines and technological advances. This surge is evidenced by a more than doubling in multi-omics scientific publications within just two years (2022-2023) since its first referenced mention in 2002, as indexed by the National Library of Medicine. This emerging field has demonstrated its capability to provide comprehensive insights into complex biological systems, representing a transformative force in health diagnostics and therapeutic strategies. However, several challenges are evident when merging varied omics data sets and methodologies, interpreting vast data dimensions, streamlining longitudinal sampling and analysis, and addressing the ethical implications of managing sensitive health information. This review evaluates these challenges while spotlighting pivotal milestones: the development of targeted sampling methods, the use of artificial intelligence in formulating health indices, the integration of sophisticated n-of-1 statistical models such as digital twins, and the incorporation of blockchain technology for heightened data security. For multi-omics to truly revolutionize healthcare, it demands rigorous validation, tangible real-world applications, and smooth integration into existing healthcare infrastructures. It is imperative to address ethical dilemmas, paving the way for the realization of a future steered by omics-informed personalized medicine.

Noninvasive prediction of lymph node metastasis in pancreatic cancer using an ultrasound-based clinicoradiomics machine learning model

OBJECTIVES

This study was designed to explore and validate the value of different machine learning models based on ultrasound image-omics features in the preoperative diagnosis of lymph node metastasis in pancreatic cancer (PC).

METHODS

This research involved 189 individuals diagnosed with PC confirmed by surgical pathology (training cohort: n = 151; test cohort: n = 38), including 50 cases of lymph node metastasis. Image-omics features were extracted from ultrasound images. After dimensionality reduction and screening, eight machine learning algorithms, including logistic regression (LR), support vector machine (SVM), K-nearest neighbors (KNN), random forest (RF), extra trees (ET), extreme gradient boosting (XGBoost), light gradient boosting machine (LightGBM), and multilayer perceptron (MLP), were used to establish image-omics models to predict lymph node metastasis in PC. The best omics prediction model was selected through ROC curve analysis. Machine learning models were used to analyze clinical features and determine variables to establish a clinical model. A combined model was constructed by combining ultrasound image-omics and clinical features. Decision curve analysis (DCA) and a nomogram were used to evaluate the clinical application value of the model.

RESULTS

A total of 1561 image-omics features were extracted from ultrasound images. 15 valuable image-omics features were determined by regularization, dimension reduction, and algorithm selection. In the image-omics model, the LR model showed higher prediction efficiency and robustness, with an area under the ROC curve (AUC) of 0.773 in the training set and an AUC of 0.850 in the test set. The clinical model constructed by the boundary of lesions in ultrasound images and the clinical feature CA199 (AUC = 0.875). The combined model had the best prediction performance, with an AUC of 0.872 in the training set and 0.918 in the test set. The combined model showed better clinical benefit according to DCA, and the nomogram score provided clinical prediction solutions.

CONCLUSION

The combined model established with clinical features has good diagnostic ability and can be used to predict lymph node metastasis in patients with PC. It is expected to provide an effective noninvasive method for clinical decision-making, thereby improving the diagnosis and treatment of PC.

Machine learning techniques to predict the risk of developing diabetic nephropathy: a literature review

Purpose

Diabetes is a major public health challenge with widespread prevalence, often leading to complications such as Diabetic Nephropathy (DN)-a chronic condition that progressively impairs kidney function. In this context, it is important to evaluate if Machine learning models can exploit the inherent temporal factor in clinical data to predict the risk of developing DN faster and more accurately than current clinical models.

Methods

Three different databases were used for this literature review: Scopus, Web of Science, and PubMed. Only articles written in English and published between January 2015 and December 2022 were included.

Results

We included 11 studies, from which we discuss a number of algorithms capable of extracting knowledge from clinical data, incorporating dynamic aspects in patient assessment, and exploring their evolution over time. We also present a comparison of the different approaches, their performance, advantages, disadvantages, interpretation, and the value that the time factor can bring to a more successful prediction of diabetic nephropathy.

Conclusion

Our analysis showed that some studies ignored the temporal factor, while others partially exploited it. Greater use of the temporal aspect inherent in Electronic Health Records (EHR) data, together with the integration of omics data, could lead to the development of more reliable and powerful predictive models.

Prediction of spontaneous preterm birth using supervised machine learning on metabolomic data: A case-cohort study

OBJECTIVES

To identify and internally validate metabolites predictive of spontaneous preterm birth (sPTB) using multiple machine learning methods and sequential maternal serum samples, and to predict spontaneous early term birth (sETB) using these metabolites.

DESIGN

Case-cohort design within a prospective cohort study.

SETTING

Cambridge, UK.

POPULATION OR SAMPLE

A total of 399 Pregnancy Outcome Prediction study participants, including 98 cases of sPTB.

METHODS

An untargeted metabolomic analysis of maternal serum samples at 12, 20, 28 and 36 weeks of gestation was performed. We applied six supervised machine learning methods and a weighted Cox model to measurements at 28 weeks of gestation and sPTB, followed by feature selection. We used logistic regression with elastic net penalty, followed by best subset selection, to reduce the number of predictive metabolites further. We applied coefficients from the chosen models to measurements from different gestational ages to predict sPTB and sETB.

MAIN OUTCOME MEASURES

sPTB and sETB.

RESULTS

We identified 47 metabolites, mostly lipids, as important predictors of sPTB by two or more methods and 22 were identified by three or more methods. The best 4-predictor model had an optimism-corrected area under the receiver operating characteristics curve (AUC) of 0.703 at 28 weeks of gestation. The model also predicted sPTB in 12-week samples (0.606, 95% CI 0.544-0.667) and 20-week samples (0.657, 95% CI 0.597-0.717) and it predicted sETB in 36-week samples (0.727, 95% CI 0.606-0.849). A lysolipid, 1-palmitoleoyl-GPE (16:1)*, was the strongest predictor of sPTB at 12 weeks of gestation (0.609, 95% CI 0.548-0.670), 20 weeks (0.630, 95% CI 0.569-0.690) and 28 weeks (0.660, 95% CI 0.599-0.722), and of sETB at 36 weeks (0.739, 95% CI 0.618-0.860).

CONCLUSIONS

We identified and internally validated maternal serum metabolites predictive of sPTB. A lysolipid, 1-palmitoleoyl-GPE (16:1)*, is a novel predictor of sPTB and sETB. Further validation in external populations is required.

Single-cell combined with transcriptome sequencing to explore the molecular mechanism of cell communication in idiopathic pulmonary fibrosis

Idiopathic pulmonary fibrosis (IPF) is a common, chronic, and progressive lung disease that severely impacts human health and survival. However, the intricate molecular underpinnings of IPF remains elusive. This study aims to delve into the nuanced molecular interplay of cellular interactions in IPF, thereby laying the groundwork for innovative therapeutic approaches in the clinical field of IPF. Sophisticated bioinformatics methods were employed to identify crucial biomarkers essential for the progression of IPF. The GSE122960 single-cell dataset was obtained from the Gene Expression Omnibus (GEO) compendium, and intercellular communication potentialities were scrutinized via CellChat. The random survival forest paradigm was established using the GSE70866 dataset. Quintessential genes were selected through Kaplan-Meier (KM) curves, while immune infiltration examinations, functional enrichment critiques and nomogram paradigms were inaugurated. Analysis of intercellular communication revealed an intimate potential connections between macrophages and various cell types, pinpointing five cardinal genes influencing the trajectory and prognosis of IPF. The nomogram paradigm, sculpted from these seminal genes, exhibits superior predictive prowess. Our research meticulously identified five critical genes, confirming their intimate association with the prognosis, immune infiltration and transcriptional governance of IPF. Interestingly, we discerned these genes' engagement with the EPITHELIAL_MESENCHYMAL_TRANSITION signalling pathway, which may enhance our understanding of the molecular complexity of IPF.

Artificial Intelligence in Point-of-Care Biosensing: Challenges and Opportunities

The integration of artificial intelligence (AI) into point-of-care (POC) biosensing has the potential to revolutionize diagnostic methodologies by offering rapid, accurate, and accessible health assessment directly at the patient level. This review paper explores the transformative impact of AI technologies on POC biosensing, emphasizing recent computational advancements, ongoing challenges, and future prospects in the field. We provide an overview of core biosensing technologies and their use at the POC, highlighting ongoing issues and challenges that may be solved with AI. We follow with an overview of AI methodologies that can be applied to biosensing, including machine learning algorithms, neural networks, and data processing frameworks that facilitate real-time analytical decision-making. We explore the applications of AI at each stage of the biosensor development process, highlighting the diverse opportunities beyond simple data analysis procedures. We include a thorough analysis of outstanding challenges in the field of AI-assisted biosensing, focusing on the technical and ethical challenges regarding the widespread adoption of these technologies, such as data security, algorithmic bias, and regulatory compliance. Through this review, we aim to emphasize the role of AI in advancing POC biosensing and inform researchers, clinicians, and policymakers about the potential of these technologies in reshaping global healthcare landscapes.

A review of the role of epigenetic studies for intramuscular fat deposition in beef cattle

Intramuscular fat (IMF) deposition profoundly influences meat quality and economic value in beef cattle production. Meanwhile, contemporary developments in epigenetics have opened new outlooks for understanding the molecular basics of IMF regulation, and it has become a key area of research for world scholars. Therefore, the aim of this paper was to provide insight and synthesis into the intricate relationship between epigenetic mechanisms and IMF deposition in beef cattle. The methodology involves a thorough analysis of existing literature, including pertinent books, academic journals, and online resources, to provide a comprehensive overview of the role of epigenetic studies in IMF deposition in beef cattle. This review summarizes the contemporary studies in epigenetic mechanisms in IMF regulation, high-resolution epigenomic mapping, single-cell epigenomics, multi-omics integration, epigenome editing approaches, longitudinal studies in cattle growth, environmental epigenetics, machine learning in epigenetics, ethical and regulatory considerations, and translation to industry practices from perspectives of IMF deposition in beef cattle. Moreover, this paper highlights DNA methylation, histone modifications, acetylation, phosphorylation, ubiquitylation, non-coding RNAs, DNA hydroxymethylation, epigenetic readers, writers, and erasers, chromatin immunoprecipitation followed by sequencing, whole genome bisulfite sequencing, epigenome-wide association studies, and their profound impact on the expression of crucial genes governing adipogenesis and lipid metabolism. Nutrition and stress also have significant influences on epigenetic modifications and IMF deposition. The key findings underscore the pivotal role of epigenetic studies in understanding and enhancing IMF deposition in beef cattle, with implications for precision livestock farming and ethical livestock management. In conclusion, this review highlights the crucial significance of epigenetic pathways and environmental factors in affecting IMF deposition in beef cattle, providing insightful information for improving the economics and meat quality of cattle production.

Mass spectrometry-based proteomic applications in dental implants research

Dental implants have been established as successful treatment options for missing teeth with steadily increasing demands. Today, the primary areas of research in dental implantology revolve around osseointegration, soft and hard tissue grafting as well as peri-implantitis diagnostics, prevention, and treatment. This review provides a comprehensive overview of the current literature on the application of MS-based proteomics in dental implant research, highlights how explorative proteomics provided insights into the biology of peri-implant soft and hard tissues and how proteomics facilitated the stratification between healthy and diseased implants, enabling the identification of potential new diagnostic markers. Additionally, this review illuminates technical aspects, and provides recommendations for future study designs based on the current evidence.

Plasma metabolomics reveals risk factors for lung adenocarcinoma

Background

Metabolic reprogramming plays a significant role in the advancement of lung adenocarcinoma (LUAD), yet the precise metabolic changes remain incompletely understood. This study aims to uncover metabolic indicators associated with the progression of LUAD.

Methods

A total of 1083 subjects were recruited, including 670 LUAD, 135 benign lung nodules (BLN) and 278 healthy controls (HC). Gas chromatography-mass spectrometry (GC/MS) was used to identify and quantify plasma metabolites. Odds ratios (ORs) were calculated to determine LUAD risk factors, and machine learning algorithms were utilized to differentiate LUAD from BLN.

Results

High levels of oxalate, glycolate, glycine, glyceric acid, aminomalonic acid, and creatinine were identified as risk factors for LUAD (adjusted ORs>1.2, P<0.03). Remarkably, oxalate emerged as a distinctive metabolic risk factor exhibiting a strong correlation with the progression of LUAD (adjusted OR=5.107, P<0.001; advanced-stage vs. early-stage). The Random Forest (RF) model demonstrated a high degree of efficacy in distinguishing between LUAD and BLN (accuracy = 1.00 and 0.73, F1-score= 1.00 and 0.79, and AUC = 1.00 and 0.76 in the training and validation sets, respectively). TCGA and GTEx gene expression data have shown that lactate dehydrogenase A (LDHA), a crucial enzyme involved in oxalate metabolism, is increasingly expressed in the progression of LUAD. High LDHA expression levels in LUAD patients are also linked to poor prognoses (HR=1.66, 95% CI=1.34-2.07, P<0.001).

Conclusions

This study reveals risk factors associated with LUAD.

Integrative analysis of RNA expression data unveils distinct cancer types through machine learning techniques

Cancer is a highly complex and heterogeneous disease. Traditional methods of cancer classification based on histopathology have limitations in guiding personalized prognosis and therapy. Gene expression profiling provides a powerful approach to unraveling molecular intricacies and better-stratifying cancer subtypes. In this study, we performed an integrative analysis of RNA sequencing data from five cancer types - BRCA, KIRC, COAD, LUAD, and PRAD. A machine learning workflow consisting of dataset identification, normalization, feature selection, dimensionality reduction, clustering, and classification was implemented. The k-means algorithm was applied to categorize samples into distinct clusters based solely on gene expression patterns. Five unique clusters emerged from the unsupervised machine learning based analysis, significantly correlating with the known cancer types. BRCA aligned predominantly with one cluster, while COAD spanned three clusters. KIRC was represented within two main clusters. LUAD is associated strongly with a single cluster and PRAD with another cluster. This demonstrates the ability of machine learning approaches to unravel complex signatures within transcriptomic profiles that can delineate cancer subtypes. The proposed study highlights the potential of integrative analytics to derive meaningful biological insights from high-dimensional omics datasets. Molecular subtyping through machine learning clustering enhances our understanding of the intrinsic heterogeneities and pathways dysregulated in different cancers. Overall, this study exemplifies a powerful computational framework to classify gene expressions of patients having different types of cancers and guide personalized therapeutic decisions. Finally, Wide Neural Network demonstrates a significantly higher accuracy, achieving 99.834% on the validation set and an even more impressive 99.995% on the test set.

AI-organoid integrated systems for biomedical studies and applications

In this review, we explore the growing role of artificial intelligence (AI) in advancing the biomedical applications of human pluripotent stem cell (hPSC)-derived organoids. Stem cell-derived organoids, these miniature organ replicas, have become essential tools for disease modeling, drug discovery, and regenerative medicine. However, analyzing the vast and intricate datasets generated from these organoids can be inefficient and error-prone. AI techniques offer a promising solution to efficiently extract insights and make predictions from diverse data types generated from microscopy images, transcriptomics, metabolomics, and proteomics. This review offers a brief overview of organoid characterization and fundamental concepts in AI while focusing on a comprehensive exploration of AI applications in organoid-based disease modeling and drug evaluation. It provides insights into the future possibilities of AI in enhancing the quality control of organoid fabrication, label-free organoid recognition, and three-dimensional image reconstruction of complex organoid structures. This review presents the challenges and potential solutions in AI-organoid integration, focusing on the establishment of reliable AI model decision-making processes and the standardization of organoid research.

Understand how machine learning impact lung cancer research from 2010 to 2021: A bibliometric analysis

Advances in lung cancer research applying machine learning (ML) technology have generated many relevant literature. However, there is absence of bibliometric analysis review that aids a comprehensive understanding of this field and its progress. Present article for the first time performed a bibliometric analysis to clarify research status and focus from 2010 to 2021. In the analysis, a total of 2,312 relevant literature were searched and retrieved from the Web of Science Core Collection database. We conducted a bibliometric analysis and further visualization. During that time, exponentially growing annual publication and our model have shown a flourishing research prospect. Annual citation reached the peak in 2017. Researchers from United States and China have produced most of the relevant literature and strongest partnership between them. Medical image analysis and Nature appeared to bring more attention to the public. The computer-aided diagnosis, precision medicine, and survival prediction were the focus of research, reflecting the development trend at that period. ML did make a big difference in lung cancer research in the past decade.

Random forest can accurately predict the technique failure of peritoneal dialysis associated peritonitis patients

Instructions

Peritoneal dialysis associated peritonitis (PDAP) is a major cause of technique failure in peritoneal dialysis (PD) patients. The purpose of this study is to construct risk prediction models by multiple machine learning (ML) algorithms and select the best one to predict technique failure in PDAP patients accurately.

Methods

This retrospective cohort study included maintenance PD patients in our center from January 1, 2010 to December 31, 2021. The risk prediction models for technique failure were constructed based on five ML algorithms: random forest (RF), the least absolute shrinkage and selection operator (LASSO), decision tree, k nearest neighbor (KNN), and logistic regression (LR). The internal validation was conducted in the test cohort.

Results

Five hundred and eight episodes of peritonitis were included in this study. The technique failure accounted for 26.38%, and the mortality rate was 4.53%. There were resignificant statistical differences between technique failure group and technique survival group in multiple baseline characteristics. The RF prediction model is the best able to predict the technique failure in PDAP patients, with the accuracy of 93.70% and area under curve (AUC) of 0.916. The sensitivity and specificity of this model was 96.67 and 86.49%, respectively.

Conclusion

RF prediction model could accurately predict the technique failure of PDAP patients, which demonstrated excellent predictive performance and may assist in clinical decision making.

Leveraging technology-driven strategies to untangle omics big data: circumventing roadblocks in clinical facets of oral cancer

Oral cancer is one of the 19most rapidly progressing cancers associated with significant mortality, owing to its extreme degree of invasiveness and aggressive inclination. The early occurrences of this cancer can be clinically deceiving leading to a poor overall survival rate. The primary concerns from a clinical perspective include delayed diagnosis, rapid disease progression, resistance to various chemotherapeutic regimens, and aggressive metastasis, which collectively pose a substantial threat to prognosis. Conventional clinical practices observed since antiquity no longer offer the best possible options to circumvent these roadblocks. The world of current cancer research has been revolutionized with the advent of state-of-the-art technology-driven strategies that offer a ray of hope in confronting said challenges by highlighting the crucial underlying molecular mechanisms and drivers. In recent years, bioinformatics and Machine Learning (ML) techniques have enhanced the possibility of early detection, evaluation of prognosis, and individualization of therapy. This review elaborates on the application of the aforesaid techniques in unraveling potential hints from omics big data to address the complexities existing in various clinical facets of oral cancer. The first section demonstrates the utilization of omics data and ML to disentangle the impediments related to diagnosis. This includes the application of technology-based strategies to optimize early detection, classification, and staging via uncovering biomarkers and molecular signatures. Furthermore, breakthrough concepts such as salivaomics-driven non-invasive biomarker discovery and omics-complemented surgical interventions are articulated in detail. In the following part, the identification of novel disease-specific targets alongside potential therapeutic agents to confront oral cancer via omics-based methodologies is presented. Additionally, a special emphasis is placed on drug resistance, precision medicine, and drug repurposing. In the final section, we discuss the research approaches oriented toward unveiling the prognostic biomarkers and constructing prediction models to capture the metastatic potential of the tumors. Overall, we intend to provide a bird's eye view of the various omics, bioinformatics, and ML approaches currently being used in oral cancer research through relevant case studies.

Systems Biology Approaches to Understanding COVID-19 Spread in the Population

In essence, the COVID-19 pandemic can be regarded as a systems biology problem, with the entire world as the system, and the human population as the element transitioning from one state to another with certain transition rates. While capturing all the relevant features of such a complex system is hardly possible, compartmental epidemiological models can be used as an appropriate simplification to model the system's dynamics and infer its important characteristics, such as basic and effective reproductive numbers of the virus. These measures can later be used as response variables in feature selection methods to uncover the main factors contributing to disease transmissibility. We here demonstrate that a combination of dynamic modeling and machine learning approaches can represent a powerful tool in understanding the spread, not only of COVID-19, but of any infectious disease of epidemiological proportions.

How is Big Data reshaping preclinical aging research?

The exponential scientific and technological progress during the past 30 years has favored the comprehensive characterization of aging processes with their multivariate nature, leading to the advent of Big Data in preclinical aging research. Spanning from molecular omics to organism-level deep phenotyping, Big Data demands large computational resources for storage and analysis, as well as new analytical tools and conceptual frameworks to gain novel insights leading to discovery. Systems biology has emerged as a paradigm that utilizes Big Data to gain insightful information enabling a better understanding of living organisms, visualized as multilayered networks of interacting molecules, cells, tissues and organs at different spatiotemporal scales. In this framework, where aging, health and disease represent emergent states from an evolving dynamic complex system, context given by, for example, strain, sex and feeding times, becomes paramount for defining the biological trajectory of an organism. Using bioinformatics and artificial intelligence, the systems biology approach is leading to remarkable advances in our understanding of the underlying mechanism of aging biology and assisting in creative experimental study designs in animal models. Future in-depth knowledge acquisition will depend on the ability to fully integrate information from different spatiotemporal scales in organisms, which will probably require the adoption of theories and methods from the field of complex systems. Here we review state-of-the-art approaches in preclinical research, with a focus on rodent models, that are leading to conceptual and/or technical advances in leveraging Big Data to understand basic aging biology and its full translational potential.

Uncovering the Gut-Liver Axis Biomarkers for Predicting Metabolic Burden in Mice

Western diet (WD) intake, aging, and inactivation of farnesoid X receptor (FXR) are risk factors for metabolic and chronic inflammation-related health issues ranging from metabolic dysfunction-associated steatotic liver disease (MASLD) to dementia. The progression of MASLD can be escalated when those risks are combined. Inactivation of FXR, the receptor for bile acid (BA), is cancer prone in both humans and mice. The current study used multi-omics including hepatic transcripts, liver, serum, and urine metabolites, hepatic BAs, as well as gut microbiota from mouse models to classify those risks using machine learning. A linear support vector machine with K-fold cross-validation was used for classification and feature selection. We have identified that increased urine sucrose alone achieved 91% accuracy in predicting WD intake. Hepatic lithocholic acid and serum pyruvate had 100% and 95% accuracy, respectively, to classify age. Urine metabolites (decreased creatinine and taurine as well as increased succinate) or increased gut bacteria (Dorea, Dehalobacterium, and Oscillospira) could predict FXR deactivation with greater than 90% accuracy. Human disease relevance is partly revealed using the metabolite-disease interaction network. Transcriptomics data were also compared with the human liver disease datasets. WD-reduced hepatic Cyp39a1 (cytochrome P450 family 39 subfamily a member 1) and increased Gramd1b (GRAM domain containing 1B) were also changed in human liver cancer and metabolic liver disease, respectively. Together, our data contribute to the identification of noninvasive biomarkers within the gut-liver axis to predict metabolic status.