Genetics

Modification and applications of glucose oxidase: optimization strategies and high-throughput screening technologies.

Glucose oxidase (GOD), an oxidoreductase (EC 1.1.3.4), catalyzes the oxidation of β-D-glucose to gluconic acid using molecular oxygen as the electron acceptor, with concomitant generation of hydrogen peroxide. Owing to its versatile catalytic properties, GOD has garnered significant attention across diverse fields, including food and beverage manufacture, agriculture, biosensors and biotechnology....

Integrative analysis of multi-omics data and gut microbiota composition reveals prognostic subtypes and predicts immunotherapy response in colorectal cancer using machine learning.

Colorectal cancer (CRC) exhibits substantial heterogeneity in molecular subtypes and clinical outcomes. We performed an integrative analysis of multi-omics data from 274 CRC patients to investigate the impact of gut microbiota composition on prognosis, identify novel subtypes, and develop a machine learning-based prognostic model. Our microbiome analysis revealed significant differences between CR...

Development and validation of a prognostic model for predicting survival and immunotherapy benefits in melanoma based on metabolism-relevant genes.

Skin cutaneous melanoma (SKCM) is a fatal form of skin cancer. Metabolism-related genes (MRGs) comprise a group of genes that possess the ability to m...

Identification and validation of epithelial‑mesenchymal transition‑related genes for diabetic nephropathy by WGCNA and machine learning.

Diabetic nephropathy (DN) is the main cause of end‑stage renal disease, with epithelial‑mesenchymal transition (EMT) serving a key role in its initiat...

Machine learning-selected minimal features drive high-accuracy rule-based antibiotic susceptibility predictions for via metagenomic sequencing.

Antimicrobial resistance (AMR) represents a critical global health challenge, demanding rapid and accurate antimicrobial susceptibility testing (AST) ...

Advancing fungal phylogenetics: integrating modern sequencing, dark taxa discovery, and machine learning.

The study of fungal genetics has undergone transformative advancements in recent decades, profoundly reshaping our understanding of fungal diversity, ...

Screening and investigating the regulatory mechanisms of oxidative stress-related biomarkers in thoracic aortic aneurysms.

Excess reactive oxygen species leading to oxidative stress has been identified as a significant factor in cardiovascular disease. However, the molecul...

Machine Learning for Genomic Profiling and Drug Discovery in Personalised Lung Cancer Therapeutics.

Lung cancer is a prevalent and lethal malignancy characterised by the uncontrolled growth of abnormal cells in lung tissues, often leading to function...

A Network-Driven Framework for Drug Response Precision Prediction of Acute Myeloid Leukemia.

Acute myeloid leukemia (AML) is a clonal malignancy of myeloid progenitor cells that demonstrates highly variable responses to current regimens, highl...

Prediction of Cerebrospinal Fluid (CSF) Pressure with Generative Adversarial Network Synthetic Plasma-CSF Biomarker Pairing.

Non-invasive intracranial pressure (ICP) monitoring can help clinicians safely and efficiently monitor spaceflight-associated neuro-ocular syndrome (S...

Random access and semantic search in DNA data storage enabled by Cas9 and machine-guided design.

DNA is a promising medium for digital data storage due to its exceptional data density and longevity. Practical DNA-based storage systems require sele...

Genome sequencing is critical for forecasting outcomes following congenital cardiac surgery.

While exome and whole genome sequencing have transformed medicine by elucidating the genetic underpinnings of both rare and common complex disorders, ...

AI based natural inhibitor targeting RPS20 for colorectal cancer treatment using integrated computational approaches.

The increasing global incidence of cancer emphasizes the vital role of machine learning algorithms and artificial intelligence (AI) in identifying nov...

A self-learning method with domain knowledge integration for intelligent welding sequence planning.

Due to the emergence of mass personalized production, intelligent welding systems must achieve high levels of productivity and flexibility. Therefore,...

Interpretive prediction of hyperuricemia and gout patients via machine learning analysis of human gut microbiome.

Hyperuricemia (HUA) and gout result from imbalances in uric acid metabolism and are closely associated with the gut microbiota. Advanced analytical me...

Attention-based multimodal deep learning for interpretable and generalizable prediction of pathological complete response in breast cancer.

BACKGROUND: Accurate prediction of pathological complete response (pCR) to neoadjuvant chemotherapy has significant clinical utility in the management...

Non-invasive identification of TKI-resistant NSCLC: a multi-model AI approach for predicting EGFR/TP53 co-mutations.

OBJECTIVES: To investigate the value of multi-model based on preoperative CT scans in predicting EGFR/TP53 co-mutation status.

TRIM29 alleviates intervertebral disc degeneration through the PI3K/AKT/mTOR pathway.

Intervertebral disc degeneration (IDD), a prevalent spinal condition linked to low back pain, has substantial genetic components, necessitating deeper...

Improving Machine Learning Prediction of ADHD Using Gene Set Polygenic Risk Scores and Risk Scores From Genetically Correlated Phenotypes.

Polygenic risk scores (PRSs), which sum the effects of SNPs throughout the genome to measure risk afforded by common genetic variants, have improved o...

Prediction of Early Neoadjuvant Chemotherapy Response of Breast Cancer through Deep Learning-based Pharmacokinetic Quantification of DCE MRI.

Purpose To improve the generalizability of pathologic complete response (pCR) prediction following neoadjuvant chemotherapy using deep learning (DL)-...