Genetics

Machine learning reveals the dynamic importance of accessory sequences for outbreak clustering.

UNLABELLED: Bacterial typing at whole-genome scales is now feasible owing to decreasing costs in high-throughput sequencing and the recent advances in computation. The unprecedented resolution of whole-genome typing is achieved by genotyping the variable segments of bacterial genomes that can fluctuate significantly in gene content. However, due to the transient and hypervariable nature of many ac...

Identification of biomarkers associated with coronary artery disease and non-alcoholic fatty liver disease by bioinformatics analysis and machine learning.

The constantly emerging evidence indicates a close association between coronary artery disease (CAD) and non-alcoholic fatty liver disease (NAFLD). However, the exact mechanisms underlying their mutual relationship remain undefined. This study aims to explore the common signature genes, potential mechanisms, diagnostic markers, and therapeutic targets for CAD and NAFLD. We downloaded CAD and NAFLD...

MAI-TargetFisher: A proteome-wide drug target prediction method synergetically enhanced by artificial intelligence and physical modeling.

Computational target identification plays a pivotal role in the drug development process. With the significant advancements of deep learning methods f...

Machine learning and molecular subtyping reveal the impact of diverse patterns of cell death on the prognosis and treatment of hepatocellular carcinoma.

Programmed cell death (PCD) is a significant factor in the progression of hepatocellular carcinoma (HCC) and might serve as a crucial marker for predi...

Detecting IDH and TERTp mutations in diffuse gliomas using H-MRS with attention deep-shallow networks.

BACKGROUND: Preoperative and noninvasive detection of isocitrate dehydrogenase (IDH) and telomerase reverse transcriptase gene promoter (TERTp) mutati...

Deep Learning-Assisted Fluorescence Single-Particle Detection of Fumonisin B Powered by Entropy-Driven Catalysis and Argonaute.

Timely and accurate detection of trace mycotoxins in agricultural products and food is significant for ensuring food safety and public health. Herein,...

Identification of ubiquitination-related key biomarkers and immune infiltration in Crohn's disease by bioinformatics analysis and machine learning.

Crohn's disease (CD) is a chronic inflammatory bowel disease with an unknown etiology. Ubiquitination plays a significant role in the pathogenesis of ...

Single-cell RNA sequencing and machine learning provide candidate drugs against drug-tolerant persister cells in colorectal cancer.

Drug resistance often stems from drug-tolerant persister (DTP) cells in cancer. These cells arise from various lineages and exhibit complex dynamics. ...

Intestinal Microbiome Modulation of Therapeutic Efficacy of Cancer Immunotherapy.

Bacteria are associated with certain cancers and may induce genetic instability and cancer progression. The gut microbiome modulates the response to c...

Recipes and ingredients for deep learning models of 3D genome folding.

Three-dimensional genome folding plays roles in gene regulation and disease. In this review, we compare and contrast recent deep learning models for p...

Analysis of the genetic basis of fiber-related traits and flowering time in upland cotton using machine learning.

Cotton is an important crop for fiber production, but the genetic basis underlying key agronomic traits, such as fiber quality and flowering days, rem...

Multiomic machine learning on lactylation for molecular typing and prognosis of lung adenocarcinoma.

To integrate machine learning and multiomic data on lactylation-related genes (LRGs) for molecular typing and prognosis prediction in lung adenocarcin...

Alzheimer's Disease: Exploring Pathophysiological Hypotheses and the Role of Machine Learning in Drug Discovery.

Alzheimer's disease (AD) is a major neurodegenerative dementia, with its complex pathophysiology challenging current treatments. Recent advancements h...

Deciphering the role of metal ion transport-related genes in T2D pathogenesis and immune cell infiltration via scRNA-seq and machine learning.

INTRODUCTION: Type 2 diabetes (T2D) is a complex metabolic disorder with significant global health implications. Understanding the molecular mechanism...

Immunolipid magnetic bead-based circulating tumor cell sorting: a novel approach for pathological staging of colorectal cancer.

OBJECTIVE: This study aimed to assess whether circulating tumor cells (CTCs) from colorectal cancer (CRC) could be used as an alternative to tissue sa...

KanCell: dissecting cellular heterogeneity in biological tissues through integrated single-cell and spatial transcriptomics.

KanCell is a deep learning model based on Kolmogorov-Arnold networks (KAN) designed to enhance cellular heterogeneity analysis by integrating single-c...

Advancing DNA Structural Analysis: A SERS Approach Free from Citrate Interference Combined with Machine Learning.

Surface-enhanced Raman spectroscopy (SERS) has become an indispensable tool for biomolecular analysis, yet the detection of DNA signals remains hinder...

Identification of potential biomarkers for 2022 Mpox virus infection: a transcriptomic network analysis and machine learning approach.

Monkeypox virus (MPXV), a zoonotic pathogen, re-emerged in 2022 with the Clade IIb variant, raising global health concerns due to its unprecedented sp...

Identifying candidate RNA-seq biomarkers for severity discrimination in chemical injuries: A machine learning and molecular dynamics approach.

INTRODUCTION: Biomarkers play a crucial role across various fields by providing insights into biological responses to interventions. High-throughput g...

Modeling gene interactions in polygenic prediction via geometric deep learning.

Polygenic risk score (PRS) is a widely used approach for predicting individuals' genetic risk of complex diseases, playing a pivotal role in advancing...