Genetics

Deep learning-based identification of N6-methyladenine sites via hybrid feature fusion and SHAP-driven feature selection.

N6-methyladenine (6 mA) is a critical epigenetic modification involved in gene regulation, genome stability, and cellular adaptation. Accurate computational identification of 6 mA sites is essential for elucidating epigenetic mechanisms and advancing disease-related research. However, existing methods are often constrained by limited feature representations and poor interpretability, hindering pre...

Identification of diagnostic and therapeutic roles of programmed cell death-related proteins in dilated cardiomyopathy: a multi-omics and experimental validation study.

Dilated cardiomyopathy (DCM) is a clinically heterogeneous cardiac disorder characterized by ventricular dilation and systolic dysfunction, with limited options for mechanism-based diagnosis and targeted therapy. Programmed cell death (PCD), encompassing apoptosis, ferroptosis, pyroptosis, and other regulated mechanisms, has been implicated in the pathogenesis of DCM, yet its diagnostic and therap...

Automated karyotyping and structural anomaly detection through a hybrid multi-stage deep learning framework integrating chromosome detection, pairwise classification, and autoencoder-based analysis.

Manual karyotype analysis remains a labor-intensive and expertise-dependent process that requires visual interpretation of chromosome banding patterns...

Identification of GCNT3 as a glycometabolism-associated biomarker in endometrial cancer.

Endometrial carcinoma (EC) incidence is increasing, with diabetes mellitus (DM) elevating EC risk. This study investigates the glycometabolism-associa...

Bufalin targets E2F2 to transcriptionally inhibit LINC01410 and suppress Wnt/β-catenin signaling in esophageal squamous cell carcinoma.

Bufalin, a main active monomer component extracted from the Traditional Chinese Medicine toad venom, exhibits potent anti-tumour activity across diver...

An explainable AI framework integrating machine and deep learning models for multi-species DNA functional group classification.

DNA functional group classification across species plays a crucial role in understanding genetic diversity, evolutionary relationships and biological ...

A Fluorescence Imaging- and Deep Learning-Based Approach for Detecting Hepatitis B Virus Integration into Host Genomes.

Hepatitis B virus (HBV) infection can lead to hepatocellular carcinoma, and HBV integration into the host genome is regularly observed in the liver of...

Large-scale cryptic proteome mining revealed potential phage-mediated host-pathogen genetic exchange in Mycobacterium tuberculosis.

BACKGROUND: Due to inevitable evolution, clinical strains of Mycobacterium tuberculosis (Mtb) exhibit distinct phenotypes and differ significantly fro...

Machine Learning-Based Study on Xin-Pi Simultaneous Treatment Formula via Drug Nanodelivery Systems Regulating Macrophage Polarization and TEAD2/PKM2 Synergistic Repair Strategy in Myocarditis.

BACKGROUND: Myocarditis, as an important cardiovascular disease, involves complex pathogenic mechanisms including immune dysregulation and metabolic d...

DNA methylation biomarkers associated with early gastric cardia carcinogenesis.

Gastric cardia cancer (GCC) is an aggressive malignancy with poor prognosis, underscoring the need for better characterization of molecular alteration...

GATA Family Transcription Factor OsGATA1 Regulates Grain Size and Brassinosteroid Biosynthesis in Rice.

Grain size is a critical agronomic trait that influences grain yield in rice. However, the molecular mechanisms underlying grain size regulation in ri...

CXCL16 promotes macrophage-driven inflammation and vascular smooth muscle cell phenotypic switching during carotid plaque destabilization.

BACKGROUND: Carotid plaque instability is a major determinant of ischemic stroke and is characterized by heightened inflammation and structural remode...

Photoacoustic-ultrasound endoscopy for assessment of rectal cancer treatment response: A prospective study with T2-weighted MRI radiomics comparison.

Accurate identification of pathological complete response (pCR) following neoadjuvant chemoradiotherapy for locally advanced rectal cancer (LARC) is c...

Machine learning-driven integration of multi-omics data uncovers methylation-dependent molecular signatures for luminal A and B breast cancer subtypes.

BACKGROUND: Breast cancer is the most common malignancy in women and includes molecular subtypes with distinct clinical outcomes, such as luminal A an...

Quality over quantity: biopsy-anchored CT radiogenomics models outperform all-lesion training in a multi-tumour cohort despite a smaller sample size.

OBJECTIVE: Radiogenomics aims to non-invasively predict tumour genotypes from imaging, but most studies assume molecular homogeneity by assigning a si...

Robust deep learning for incomplete MRI sequences in glioma grading and IDH mutation status prediction: a large-scale multicenter study.

OBJECTIVES: Incomplete MRI sequences pose a significant challenge to the reliability of multiparametric MRI (mp-MRI) radiomics models. This study aime...

Transcriptomic Analysis and Multiple Machine Learning Approaches Identify ZDHHC20 and Its Highly Correlated Gene AK5 as Biomarkers in Multiple System Atrophy.

Multiple system atrophy (MSA) is a fatal neurodegenerative disorder lacking effective diagnostic tools. While protein palmitoylation is crucial for ne...

Whole genome methylation profiling of menstrual stem cells identifies novel biomarkers for endometriosis.

BACKGROUND: Endometriosis, despite its high prevalence, is underdiagnosed and poorly managed due to lack of clinically validated biomarkers and pathop...

Enhancing Drug Response Prediction in Epilepsy with Emerging Multimodal Models: Focus on Clinical, Pharmacologic, and Genomic Factors.

Epilepsy is a common neurological disorder, with approximately one-third of the affected population developing drug-resistant epilepsy despite the exp...

Early laboratory-based differentiation of severe influenza pneumonia and influenza-associated encephalopathy in children using a multi-model, stability-aware machine-learning workflow: a single-center retrospective cohort study.

BACKGROUND: Influenza-associated encephalopathy and acute necrotizing encephalopathy (ANE) are rare but devastating complications of pediatric influen...