Genetics

Predictive modeling of urinary 8-hydroxy-2´-deoxyguanosine (8-OHdG) in metal workers: A comparative study of machine learning algorithms in toxicology.

Urinary 8-hydroxy-2´-deoxyguanosine (8-OHdG), a biomarker for oxidative DNA damage, is commonly used to assess the repair of reactive oxygen species (ROS) induced DNA damage. This study developed predictive models to quantify 8-OHdG concentrations in urine samples based on demographic and exposure-related variables from metal workers (21.27 ng/ml) and controls (12.63 ng/ml) using ELISA and machine...

Resolving variants of uncertain significance in neurofibromatosis: An integrated approach combining deep learning and minigene assays.

Neurofibromatosis (NF) comprises genetic disorders mainly caused by pathogenic variants, yet its phenotypic and genotypic heterogeneity complicates diagnosis. We analyzed clinical and genomic data from 97 NF patients using targeted panels, whole-exome sequencing (WES), and whole-genome sequencing (WGS) from June 2020 to October 2024. Variants were classified according to established guidelines, an...

Machine Learning-Guided Survival Prediction and Treatment Sequencing in Advanced Soft Tissue Sarcoma Beyond Second-Line Therapy: A Retrospective Cohort Study.

INTRODUCTION: Evidence guiding the optimal sequencing of later-line systemic therapy in advanced soft tissue sarcoma (STS) remains limited. The aim of...

Deep learning for H&E-based meningioma molecular classification and outcome prediction: a retrospective cohort study.

BACKGROUND: The introduction of genomic profiling as a tool for molecular classification and clinical outcome prediction has revolutionised the care o...

Multidimensional proteomics and explainable AI feature selection identify cross-platform lung cancer molecular signature in blood plasma.

BACKGROUND: Lung cancer is the leading cause of cancer mortality worldwide despite the availability of low-dose computed tomography (LDCT) for screeni...

Multiscale learning of gene network-driven phenotypic dynamics of single cells.

Understanding how gene regulatory networks (GRNs) dynamically orchestrate cell fate emergence remains a fundamental challenge. Here, we present GRNvel...

Deconstructing cancer in 3D: models, mechanisms, and personalized solutions.

Three-dimensional (3D) cancer models, notably patient-derived organoids (PDOs), address the critical limitations of traditional preclinical systems, i...

Identification MYD88 as a candidate molecule of sodium overload-induced cell death in myocardial ischemia-reperfusion injury.

BACKGROUND: Myocardial ischemia-reperfusion (I/R) injury disrupts intracellular ion homeostasis, triggering intracellular sodium overload and ultimate...

Identification of potential key efferocytosis-related genes in rheumatoid arthritis using machine learning.

BACKGROUND: Rheumatoid arthritis (RA) is a chronic autoimmune disease characterized by synovial inflammation and joint destruction. Efferocytosis is a...

Decoding neuron-specific lineage to identify diagnostic biomarkers and therapeutic targets for ischemic stroke.

Ischemic stroke (IS) imposes a major global health burden. To uncover new diagnostic and therapeutic targets, we profiled neuronal heterogeneity durin...

A decision-oriented framework for genomic testing across the prostate cancer continuum.

Genomic testing is now embedded in contemporary prostate cancer care, yet the clinical meaning of different genomic platforms varies substantially by ...

Benchmarking reveals the superiority of nucleic acid foundation models in predicting lncRNA coding potential.

BACKGROUND: A subset of long noncoding RNAs (lncRNAs) contains short open reading frames and can encode functional micropeptides. However, identifying...

Understanding resistance in glioblastoma: insights into personalized and targeted therapeutic strategies.

Glioblastoma multiforme (GBM) is the most common and aggressive primary malignant brain tumor. Despite combined treatments, including surgical removal...

Multi-omics analysis identifies CD48 and ITIH5 as potential diagnostic biomarkers and therapeutic targets for metabolic disorders associated with obesity.

BACKGROUND: Obesity-driven metabolic disorders involve pathological remodeling of white adipose tissue (WAT), wherein fibroblasts coordinate fibrosis ...

Machine learning, bioinformatics analysis and chemical screening streamline target validation by identifying RNA helicase as a druggable essential protein in tobacco mosaic virus.

INTRODUCTION: Plant viruses inflict annual economic losses exceeding $30 billion and pose a significant threat to global food security. Discovering re...

Machine Learning Reveals the Contribution of Rare Genetic Variants and Enhances Risk Prediction for Coronary Artery Disease in the Japanese Population.

BACKGROUND: GWASs (genome-wide association studies) have advanced our understanding of coronary artery disease (CAD) genetics and enabled the developm...

Predicting Treatment Response After Total Neoadjuvant Therapy for Locally Advanced Rectal Cancer.

OBJECTIVE: To develop a predictive model for pathological complete response (pCR) after total neoadjuvant therapy (TNT) to inform selection for watch-...

3D chromatin architecture-related genes orchestrate LUAD evolution and therapy resistance: insights from integrative machine learning and spatial single-cell mapping.

BACKGROUND: Lung adenocarcinoma (LUAD) is a prevalent and lethal malignancy. The three-dimensional (3D) chromatin architecture significantly influence...

Quantitative RNA pseudouridine landscape reveals dynamic modification patterns and evolutionary conservation across bacterial species.

Pseudouridine (Ψ) modifications are the most abundant RNA modifications; however, their distribution and functional significance in bacteria remain la...

Deep learning of functional perturbations from condensate morphology.

Biomolecular condensates compartmentalize the interior of cells to organize complex functions, yet linking molecular interactions within condensates t...