Genetics

Integrated machine learning framework identifies EPSTI1 as a key diagnostic biomarker for Sjögren's disease: multi-cohort transcriptomic validation and single-cell characterization.

OBJECTIVE: This study aimed to develop a robust transcriptomic diagnostic signature for Sjögren's disease (SjD; formerly Sjögren's syndrome) and elucidate key biomarker functions by integrating machine learning and single-cell analysis. METHODS: Three SjD peripheral blood datasets (GSE143153, GSE51092, GSE66795; n = 414) were integrated for training, with GSE84844 and GSE40611 for external validat...

How WRKY transcription factors fine-tune specificity in plant stress responses: from W-box to regulatory code.

WRKY transcription factors are among the largest plant-specific transcription factor families and play central roles in coordinating gene expression during biotic and abiotic stress. Despite decades of research, a fundamental paradox remains: WRKY proteins bind short, widely distributed W-box cis-elements yet generate highly selective, context-dependent transcriptional outputs in vivo. In this rev...

A scalable deep-learning framework for cancer detection using cell-free DNA shallow whole-genome sequencing.

Cell-free DNA (cfDNA) in body fluids enables noninvasive cancer detection. Multifeature artificial intelligence (AI) can improve sensitivity by integr...

Advancing stress biotechnology for robust sustainable bioproduction.

Environmental stressors compromise the economic viability of microbial bioproduction, making enhanced microbial robustness essential. While traditiona...

Pulmonary nodule prediction in the multi-omics era: integrating radiomics, AI, liquid biopsy, and airway classifiers.

Low-dose CT (LDCT) lung cancer screening significantly reduces mortality but has dramatically increased the detection of pulmonary nodules. Most of th...

Novel approach to early prediction of alzheimer's disease progression using integrated deep regulatory genetic neural network and optimized deep belief networks.

UNLABELLED: To enhance the prediction progression of Alzheimer's Disease is very excavating process. PROBLEM STATEMENT: It is often very difficult to ...

Uncovering the mechanism of DCLK3 in colorectal cancer progression through WGCNA and machine learning.

BACKGROUND: Colorectal cancer (CRC) is a prevalent malignant tumor of the digestive tract with high morbidity and mortality rates. Although doublecort...

Bioinformatics and multi-omics approaches in male infertility: implications for diagnosis and assisted reproduction.

Male infertility accounts for approximately 40-50% of infertility cases worldwide and represents a complex, multifactorial condition influenced by gen...

A 2004-2025 Bibliometric Study of Genetic Variation and Multiomics Biomarkers in Sepsis Based on 940 Publications.

Sepsis is a highly heterogeneous syndrome, and conventional clinical indicators and single biomarkers often fail to capture its biological complexity ...

Bioinformatics analysis reveals the association of bile acid metabolism-related genes with sepsis.

BACKGROUND: Sepsis, is a life-threatening syndrome triggered by infection. Bile acid metabolism may be involved in the pathogenesis of sepsis, the und...

Machine learning-based analysis of drug resistance mutations in Mycobacterium tuberculosis.

Tuberculosis is a deadly airborne disease caused by Mycobacterium tuberculosis. Drug-resistant tuberculosis presents significant challenges for treatm...

Gut microbiota biomarkers of chronic kidney disease progression identified by 16S rDNA sequencing and machine learning.

Chronic kidney disease (CKD) is a global health concern characterized by high prevalence and mortality rates, yet its underlying pathogenesis remains ...

Machine Learning-Based Identification of Survival-Associated CpG Biomarkers in Pancreatic Ductal Adenocarcinoma.

Pancreatic ductal adenocarcinoma (PDAC) is an exceptionally aggressive cancer with a 5-year survival rate of less than 10%, driven by late-stage diagn...

Unveiling Large-Scale Kinase-Centric Protein-Protein Interactions through a Knowledge-Informed Workflow.

Protein phosphorylation regulates signaling, yet atomic-level substrate specificity remains elusive due to sparse structural data and phosphorylation-...

Artificial intelligence in clinical genetics: current practice and attitudes among the clinical genetics workforce.

PURPOSE: As clinical genetics artificial intelligence (AI) applications and capabilities are on a rapid rise, this study sought to assess workforce re...

Integrated multi-omics analysis identifies key microglial subpopulations and therapeutic targets in Parkinson's disease.

BACKGROUND: Parkinson's disease (PD) is a rapidly growing global health concern, with aging populations driving increasing prevalence. While neuronal ...

Epigenetic editing approaches maturity: AI-driven precision design, delivery innovation, and the road to clinical translation.

Epigenetic editing achieves durable gene silencing through targeted modification of chromatin and DNA methylation states without altering the genomic ...

Genomic profiling as an option for ovarian cancer diagnostics.

INTRODUCTION: Ovarian cancer (OC) is a highly heterogeneous and lethal gynecological malignancy. Precision oncology has shifted the management paradig...

Colorectal Cancer Screening in Hereditary and Familial High-Risk Populations: Best Practices and Future Directions.

Colorectal cancer (CRC) remains a leading cause of cancer-related morbidity and mortality worldwide yet is largely preventable through effective scree...

Using Deep Learning Models as a Genetic Architecture for the Simulation of Breeding Schemes.

In several simulation studies, long-term selection led to the rapid depletion of genetic variance. These outcomes differ from real-life observations t...