Genetics

Genome-wide modelling of plant transcription factor binding captures regulatory variants associated with phenotypic traits.

The sequence-specific recognition of cis-regulatory elements (CRE) by transcription factors (TF) propagates genotype information to phenotypes. Understanding how genetic variation affects gene regulation remains limited by the diversity and complexity of CRE interactions. Here, we address this challenge using an explainable multi-label deep learning model trained on A. thaliana DNA-binding data to...

Machine learning-based model for prediction of carbamazepine- and allopurinol-induced severe cutaneous adverse reactions in Vietnamese.

BACKGROUND: The low positive predictive value of HLA-B∗15:02 and HLA-B∗58:01 for risk stratification of carbamazepine (CBZ) and allopurinol (ALLO) induced severe cutaneous adverse reactions (SCARs) suggests that we need a better model to prevent cases. OBJECTIVE: This study aims to comprehensively investigate the role of genomic factors in CBZ- and ALLO-induced SCARs using machine-learning. METHOD...

Prospective germline exome and machine learning-based risk score identify predictive and prognostic biomarkers of immunotherapy outcomes in advanced non-small cell lung cancer.

BACKGROUND: Immune checkpoint inhibitors (ICIs) have transformed the treatment landscape of advanced non-small cell lung cancer (NSCLC). However, a su...

Prediction of circular RNA-RNA binding protein binding sites based on structural feature and dynamic feature screening.

circular RNA (circRNA)-RNA binding protein (RBP) interactions play critical roles in various diseases, and predicting their binding sites can elucidat...

Pan-pathogen vaccine approaches: Toward broad-spectrum immunity in a One Health era.

Emerging and re-emerging infectious diseases (EIDs) represent an escalating threat to global public health, as exemplified by outbreaks of COVID-19, E...

Autogenic transitions in individuality.

Major evolutionary transitions in individuality occur when previously independent entities become components of a new unit whose parts share a reprodu...

ProSiteHunter: A Unified Framework for Sequence-Based Prediction of Protein-Nucleic Acid and Protein-Protein Binding Sites.

Accurate identification of protein binding sites is essential for elucidating protein function, decoding molecular recognition, and guiding drug desig...

A Foundation Model Based CT Biomarker for Non-Invasive Prediction of Response to Neoadjuvant Immunochemotherapy in Non-Small Cell Lung Cancer.

Predicting pathological complete response (pCR) to neoadjuvant immunochemotherapy in non-small cell lung cancer (NSCLC) is clinically important yet re...

Spatial omics of immunity: Mapping cellular landscapes in tissue microenvironments.

The tumor microenvironment (TME) is composed of diverse heterogeneous components and plays a crucial role in immune cell infiltration, immune evasion,...

Genome-guided generative adversarial learning enables nanopore adaptive sequencing.

Nanopore adaptive sequencing enables real-time target enrichment, yet current deep-learning methods require costly, sample-specific experimental train...

Machine learning-based identification of high-risk triple-negative breast cancer patients despite pathological complete response after neoadjuvant chemotherapy.

The binary pathological complete response (pCR) status of triple-negative breast cancer (TNBC) patients after neoadjuvant chemotherapy (NAC) inadequat...

Metab8D: a metabolic regulome network from multiomics and machine learning.

To explore multiomic regulation of the metabolome, we used machine learning to predict metabolomic variation across ~1000 different cancer cell lines ...

Integrating network toxicology, machine learning, and single-cell sequencing to reveal the FASN-mediated role of phenolic endocrine disruptors in water in promoting prostate cancer.

BACKGROUND: Phenolic endocrine-disrupting chemicals (EDCs) like nonylphenol (NP) and octylphenol (OP) are widespread water pollutants. Their estrogen-...

Transcriptome sequencing combined with experimental verification to explore potential key genes related to uric acid in diabetic retinopathy.

PURPOSE: Diabetic retinopathy (DR), a major microvascular complication of diabetes and leading global blindness cause, involves uric acid (UA) in its ...

Performance comparison of a neuro-symbolic large language model system versus conventional AI models and human experts in cholangitis management.

BACKGROUND: Large language models (LLMs) have shown promising results in medical decision support; Background: Large language models (LLMs) have demon...

Unraveling 'F' factor: towards a genetic-clinical framework for the musculoskeletal-heart crosstalk in metabolic aging.

BACKGROUND: The rising co-occurrence of cardiometabolic diseases and musculoskeletal degeneration poses a critical challenge to healthy aging, yet the...

Consensus Pituitary Atlas, a scalable resource for annotation, novel marker discovery, and analyses in mouse pituitary gland research.

Previous single-cell profiling studies of the pituitary gland have yielded minimally reproducible insights due to their low statistical power and meth...

Integrated Network Pharmacology and Machine Learning to Reveal the Mechanisms of Schisandrin A Against Triple-Negative Breast Cancer.

This study aimed to elucidate the mechanism of action of Schisandrin A in the intervention of triple-negative breast cancer (TNBC). Through the applic...

Diagnosis and classification of thalassemia disease using machine learning: Comparative analysis of traditional models and a novel hybrid approach.

Thalassemia is a hereditary blood disorder characterized by abnormal hemoglobin production. Common diagnostic methods include complete blood count, hi...

Identification of Polyamine Metabolism-Related Biomarkers in Psoriasis: A Study Based on Transcriptomics and Single-Cell Sequencing.

Psoriasis (PS) is linked to altered polyamine metabolism, which may drive chronic inflammation and abnormal keratinocyte proliferation. We integrated ...