Genetics

Deep learning-driven MRI radiomics reveals biological subtypes and predicts recurrence risk in rectal cancer.

Traditional TNM staging inadequately captures the recurrence risk of rectal cancer (RC), limiting prognostic accuracy and personalized treatment decisions. Here, we developed an MRI-based deep learning approach to predict recurrence risk and leveraged pathology and transcriptomic analyses to characterize tumor heterogeneity and provide biological interpretation. In this multicenter study of 2060 p...

Advances and challenges of splicing prediction with AI.

RNA alternative splicing is a fundamental post-transcriptional mechanism whose dysregulation drives various human diseases. Predicting splicing outcomes is therefore a central challenge in precision medicine. This Review traces the evolution of computational approaches from early statistical heuristics to modern artificial intelligence frameworks. We dissect the methodologies that shape predictive...

Integrative multi-omics and single-cell analysis identifies EGFR pathway activation and metabolic reprogramming as potential synthetic lethal vulnerabilities in resistance to the FGFR inhibitor AZD4547.

BACKGROUND: Although fibroblast growth factor receptor (FGFR) inhibitors (FGFRi) have demonstrated clinical promise, the inevitable emergence of acqui...

A fatty acid metabolism-based deep learning model predicts biochemical recurrence and identifies NUDT19 as a candidate metabolic factor in prostate cancer.

PURPOSE: To develop a fatty acid metabolism-based deep learning model for predicting biochemical recurrence (BCR) in prostate cancer (PCa) and to iden...

Programmed cell death-related gene S100A9 promotes macrophage M1 polarization and chondrocyte apoptosis in rheumatoid arthritis.

BACKGROUND: Rheumatoid arthritis (RA) is a heterogeneous chronic autoimmune disease. Its high disability rate has a serious impact on individuals and ...

Identification of T-cell exhaustion-related biomarkers in aortic dissection via integrated multi-omics analysis and mendelian randomization.

BACKGROUND: Aortic dissection (AD) is a life-threatening cardiovascular emergency characterized by high acute mortality. While immune dysregulation is...

Integrative transcriptomic and CRISPR dependency analysis identifies hepatoblastoma-specific essential genes and actionable vulnerabilities.

BACKGROUND: Hepatoblastoma (HB) is the most common primary liver malignancy in childhood, yet its molecular determinants, functional dependencies, and...

Image feature embedding with a deep learning framework improves genome-wide association studies on dog endophenotypes.

Domestic dogs exhibit substantial morphological diversity, making quantitative characterization of their phenotypes challenging. Traditional phenotypi...

ACOT9, a mitochondrial metabolism-related gene, promotes ROS-associated epithelial remodeling in laryngeal squamous cell carcinoma.

BACKGROUND: Laryngeal squamous cell carcinoma (LSCC) is characterized by mitochondrial metabolic reprogramming, but its prognostic significance and un...

Development and evaluation of an explainable machine learning selection pipeline for predicting neoadjuvant chemoradiotherapy response in locally advanced rectal cancer.

BACKGROUND: The selection of appropriate machine learning (ML) methods for clinical research remains challenging, particularly when both predictive pe...

SinTransNet: an EEG-based deep learning framework for infantile epileptic spasms syndrome detection.

Infantile Epileptic Spasms Syndrome (IESS) represents a severe form of developmental epileptic encephalopathy in infancy, characterized by clusters of...

Engaging students in an AI-driven RNA drug design research project through a crowd science-infused learning approach.

The rapid growth of RNA therapeutics and artificial intelligence (AI) has transformed antiviral drug discovery and created an urgent need for interdis...

Impact on Cost and Expert Time of Data-Efficient Deep Learning for Medical Image Segmentation.

Purpose To develop and systematically evaluate an iterative training approach, termed the expert-guided annotation loop, for efficient reference stand...

Identification of Diagnostic Biomarkers Among Metabolism-related Genes in Rheumatoid Arthritis: Insights into Immune Landscape and Molecular Subtype Characterization.

Rheumatoid arthritis (RA) is a chronic autoimmune disease characterized by synovial inflammation and joint destruction. Metabolic reprogramming and im...

High-fidelity super-resolution CT radiomics for non-invasive EGFR mutation prediction in lung adenocarcinoma: a multi-center pooled analysis.

OBJECTIVE: To develop and validate a high-fidelity super-resolution (SR)-enhanced radiomics framework using a Residual Channel Attention Network (RCAN...

FFixR: A Machine Learning Framework for Accurate Somatic Mutation Calling from FFPE RNA-Seq Data in Cancer.

MOTIVATION: Formalin-fixed paraffin-embedded (FFPE) tissues are widely used in clinical and research settings, yet their use for detecting somatic mut...

Marfan Syndrome Prediction Via Graph Neural Networks on 3D Facial Cues.

Marfan syndrome (MFS) is a rare genetic connective tissue disorder whose early detection is critical to prevent life-threatening cardiovascular compli...

Predictive value of PfEMP1 antibody profiles for the course of controlled human malaria infections.

Antibody-mediated immunity directed against Plasmodium falciparum erythrocyte membrane protein 1 (PfEMP1) is an important immune mechanism for protect...

AI-CURA, an automated LLM workflow for high-accuracy genetic variant classification.

Large language models (LLMs) have been extensively tested for incorporation into medical applications in recent years; however, their potential in cli...

DNA origami snaps into place.

An electrically controlled DNA origami snap-through switch provides robust, programmable logic for molecular robotics.