Genetics

Galactose-Induced Cataracts in Rats: A Machine Learning Analysis.

Rat models are widely used to study cataracts due to their cost-effectiveness and prominent physiological and genetic similarities to humans The objective of this study was to identify genes involved in cataractogenesis due to galactose exposure in rats. We analyzed four datasets from the Gene Expression Omnibus, including both and models of cataracts in different rat strains. Feature selectio...

Integration of single-cell and bulk RNA sequencing data using machine learning identifies oxidative stress-related genes LUM and PCOLCE2 as potential biomarkers for heart failure.

Oxidative stress (OS) is a pivotal mechanism driving the progression of cardiovascular diseases, particularly heart failure (HF). However, the comprehensive characterisation of OS-related genes in HF remains largely unexplored. In the present study, we analysed single-cell RNA sequencing datasets from the Gene Expression Omnibus and OS gene sets from GeneCards. We identified 167 OS-related genes p...

HybProm: An attention-assisted hybrid CNN-BiLSTM model for the interpretable prediction of DNA promoter.

Promoter prediction is essential for analyzing gene structures, understanding regulatory networks, transcription mechanisms, and precisely controlling...

Unravelling single-cell DNA replication timing dynamics using machine learning reveals heterogeneity in cancer progression.

Genomic heterogeneity has largely been overlooked in single-cell replication timing (scRT) studies. Here, we develop MnM, an efficient machine learnin...

Adaptive genetic algorithm based deep feature selector for cancer detection in lung histopathological images.

Cancer is a global health concern because of a significant mortality rate and a wide range of affected organs. Early detection and accurate classifica...

Unlocking precision medicine: clinical applications of integrating health records, genetics, and immunology through artificial intelligence.

Artificial intelligence (AI) has emerged as a transformative force in precision medicine, revolutionizing the integration and analysis of health recor...

Genome data based deep learning identified new genes predicting pharmacological treatment response of attention deficit hyperactivity disorder.

Although the efficacy of pharmacy in the treatment of attention deficit/hyperactivity disorder (ADHD) has been well established, the lack of predictor...

Using near-infrared hyperspectral imaging combined with machine learning to predict the components and the origin of Radix Paeoniae Rubra.

The efficacy and safety of drugs are closely related to the geographical origin and quality of the raw materials. This study focuses on using near-inf...

An Information Fusion System-Driven Deep Neural Networks With Application to Cancer Mortality Risk Estimate.

Next-generation sequencing (NGS) genomic data offer valuable high-throughput genomic information for computational applications in medicine. Using gen...

Transfer Learning Prediction of Early Exposures and Genetic Risk Score on Adult Obesity in Two Minority Cohorts.

Due to ethnic heterogeneity in genetic architecture, genetic risk score (GRS) constructed within the European population generally possesses poor port...

Ontology-based expansion of virtual gene panels to improve diagnostic efficiency for rare genetic diseases.

BACKGROUND: Virtual Gene Panels (VGP) comprising disease-associated causal genes are utilized in the diagnosis of rare genetic diseases to evaluate ca...

Deep Learning and Single-Molecule Localization Microscopy Reveal Nanoscopic Dynamics of DNA Entanglement Loci.

Understanding molecular dynamics at the nanoscale remains challenging due to limitations in the temporal resolution of current imaging techniques. Dee...

Machine learning-random forest model was used to construct gene signature associated with cuproptosis to predict the prognosis of gastric cancer.

Gastric cancer (GC) is one of the most common tumors; one of the reasons for its poor prognosis is that GC cells can resist normal cell death process ...

Transitioning from wet lab to artificial intelligence: a systematic review of AI predictors in CRISPR.

The revolutionary CRISPR-Cas9 system leverages a programmable guide RNA (gRNA) and Cas9 proteins to precisely cleave problematic regions within DNA se...

The external validity of machine learning-based prediction scores from hematological parameters of COVID-19: A study using hospital records from Brazil, Italy, and Western Europe.

The unprecedented worldwide pandemic caused by COVID-19 has motivated several research groups to develop machine-learning based approaches that aim to...

Radiomics in glioma: emerging trends and challenges.

Radiomics is a promising neuroimaging technique for extracting and analyzing quantitative glioma features. This review discusses the application, emer...

Deep learning powered single-cell clustering framework with enhanced accuracy and stability.

Single-cell RNA sequencing (scRNA-seq) has revolutionized the field of cellular diversity research. Unsupervised clustering, a key technique in this e...

A mechanism-informed deep neural network enables prioritization of regulators that drive cell state transitions.

Cells are regulated at multiple levels, from regulations of individual genes to interactions across multiple genes. Some recent neural network models ...

Leveraging AI to automate detection and quantification of extrachromosomal DNA to decode drug responses.

INTRODUCTION: Traditional drug discovery efforts primarily target rapid, reversible protein-mediated adaptations to counteract cancer cell resistance....

Deep learning to decode sites of RNA translation in normal and cancerous tissues.

The biological process of RNA translation is fundamental to cellular life and has wide-ranging implications for human disease. Accurate delineation of...