Genetics

PathInHydro, a Set of Machine Learning Models to Identify Unbinding Pathways of Gas Molecules in [NiFe] Hydrogenases.

Machine learning (ML) is a powerful tool for the automated data analysis of molecular dynamics (MD) simulations. Recent studies showed that ML models can be used to identify protein-ligand unbinding pathways and understand the underlying mechanism. To expedite the examination of MD simulations, we constructed PathInHydro, a set of supervised ML models capable of automatically assigning unbinding p...

Enhanced Sampling Simulations of RNA-Peptide Binding Using Deep Learning Collective Variables.

Enhanced sampling (ES) simulations of biomolecular recognition, such as binding small molecules to proteins and nucleic acid targets, protein-protein association, and protein-nucleic acid interactions, have gained significant attention in the simulation community because of their ability to sample long-time scale processes. However, a key challenge in implementing collective variable (CV)-based en...

Uncovering the epigenetic regulatory clues of PRRT1 in Alzheimer's disease: a strategy integrating multi-omics analysis with explainable machine learning.

BACKGROUND: Alzheimer's disease (AD) is a complex neurodegenerative disorder with a largely unexplored epigenetic landscape.

Multi-scale feature fusion of deep convolutional neural networks on cancerous tumor detection and classification using biomedical images.

In the present scenario, cancerous tumours are common in humans due to major changes in nearby environments. Skin cancer is a considerable disease det...

Deep Drug Synergy Prediction Network Using Modified Triangular Mutation-Based Differential Evolution.

Drug combination therapy is crucial in cancer treatment, but accurately predicting drug synergy remains a challenge due to the complexity of drug comb...

Partial-Label Contrastive Representation Learning for Fine-Grained Biomarkers Prediction From Histopathology Whole Slide Images.

In the domain of histopathology analysis, existing representation learning methods for biomarkers prediction from whole slide images (WSIs) face chall...

Hierarchical Graph Transformer With Contrastive Learning for Gene Regulatory Network Inference.

Gene regulatory networks (GRNs) are crucial for understanding gene regulation and cellular processes. Inferring GRNs helps uncover regulatory pathways...

Applied pharmacogenetics to predict response to treatment of first psychotic episode: study protocol.

The application of personalized medicine in patients with first-episode psychosis (FEP) requires tools for classifying patients according to their res...

Plasma Epstein-Barr Virus DNA load for diagnostic and prognostic assessment in intestinal Epstein-Barr Virus infection.

BACKGROUND: The prospective application of plasma Epstein-Barr virus (EBV) DNA load as a noninvasive measure of intestinal EBV infection remains unexp...

Deep learning captures the effect of epistasis in multifactorial diseases.

BACKGROUND: Polygenic risk score (PRS) prediction is widely used to assess the risk of diagnosis and progression of many diseases. Routinely, the weig...

Exploring the shared gene signatures and mechanism among three autoimmune diseases by bulk RNA sequencing integrated with single-cell RNA sequencing analysis.

BACKGROUND: Emerging evidence underscores the comorbidity mechanisms among autoimmune diseases (AIDs), with innovative technologies such as single-cel...

Recent advances in deep learning and language models for studying the microbiome.

Recent advancements in deep learning, particularly large language models (LLMs), made a significant impact on how researchers study microbiome and met...

Effects of feeding of vitamin C on embryonic development, hatching process, and chick rectal temperature of broiler embryos.

Maternal nutritional status plays a crucial role in embryonic development and has persistent effects on postnatal chicks. Vitamin C (VC) plays an impo...

SequenceCraft: machine learning-based resource for exploratory analysis of RNA-cleaving deoxyribozymes.

BACKGROUND: Deoxyribozymes or DNAzymes represent artificial short DNA sequences bearing many catalytic properties. In particular, DNAzymes able to cle...

Identification of key genes affecting intramuscular fat deposition in pigs using machine learning models.

Intramuscular fat (IMF) is an important indicator for evaluating meat quality. Transcriptome sequencing (RNA-seq) is widely used for the study of IMF ...

High performance COVID-19 screening using machine learning.

Since the World Health Organization declared the Coronavirus Disease 2019 (COVID-19) pandemic as an international concern of public health emergency i...

Mind the Gap: A Neural Network Framework for Imputing Genotypes in Non-Model Species.

Reduced representation sequencing (RRS) has proven to be a cost-effective solution for sequencing subsets of the genome in non-model species for large...

Transcriptomic profiling and machine learning reveal novel RNA signatures for enhanced molecular characterization of Hashimoto's thyroiditis.

While ultrasonography effectively diagnoses Hashimoto's thyroiditis (HT), exploring its transcriptomic landscape could reveal valuable insights into d...

Common biomarkers of idiopathic pulmonary fibrosis and systemic sclerosis based on WGCNA and machine learning.

Interstitial lung disease (ILD) is known to be a major complication of systemic sclerosis (SSc) and a leading cause of death in SSc patients. As the m...

Metabolic reprogramming and macrophage expansion define ACPA-negative rheumatoid arthritis: insights from single-cell RNA sequencing.

BACKGROUND: Anti-citrullinated peptide antibodies (ACPA)-negative (ACPA-) rheumatoid arthritis (RA) presents significant diagnostic and therapeutic ch...