Bioinformatics and Genomic Networks

This paper explores using ChatGPT in Reactome curation to automate pathway annotation, comparing its outputs to manual curation and identifying challenges and improvements.

CellGO uses deep learning to analyze gene function in specific cell types, identifying active pathways and their roles in disease.

This paper constructs a comprehensive dataset of triadic high-order protein interactions and demonstrates their value over pairwise interactions in understanding biological networks.

Ensemble-GNN enables federated, ensemble-based graph neural networks for disease module discovery using patient-specific PPI networks and omics data.

hipFG harmonizes and integrates diverse functional genomics data into standardized, searchable datasets for analysis workflows.

This study compares linear and non-linear joint embedding methods for bulk and single-cell multi-omics, finding non-linear methods excel in imputation and joint embeddings improve performance in supervised tasks.

WhatIsMyGene demonstrates that lab-generated datasets often outperform curated gene lists in gene enrichment analysis and provides tools to improve result ranking.

The paper introduces hypergraphs and centrality measures to identify key features in gene expression data using Senecio lautus plant data.

The paper introduces GraCoal embedding to improve functional organization analysis of scale-free biological networks by incorporating graphlet patterns, outperforming traditional Spring embedding methods.

CONE generates context-specific gene embeddings from context-free interactomes to identify disease-associated genes using contextualized random walks and graph attention networks.

MOAL automates omic data analysis tasks to produce reproducible bioanalysis results using an R package.

CoRegNet constructs gene co-regulation networks from public RNA-Seq data using a beta-binomial model, outperforming existing methods and identifying non-linear and Simpson's paradox gene pairs.

VIBE is an R package that enables pathway-guided RNA-seq analysis for disease stratification and therapeutic targeting in various disorders.

Rummagene extracts and provides access to gene sets from PMC publications for search and analysis, revealing trends and enabling functional predictions.

The paper uses phenomics and machine learning to predict protein functions in fission yeast, identifying novel roles and validating predictions through experimental validation.

This study evaluates dimensionality reduction methods to normalize CRISPR screen data and improve functional gene network extraction from the DepMap.

This study extends protein interaction networks by incorporating small molecules, protein isoforms, and post-translational modifications to better represent biological systems.

SENSE-PPI uses deep learning to predict protein-protein interactions across species and between proteins, showing high accuracy and versatility.

GeneSetR is a web server that analyzes gene lists from Perturb-Seq data to identify genotype-phenotype relationships through clustering, network analysis, and gene signature identification.

HPC-Atlas constructs a comprehensive atlas of human protein complexes by integrating interaction networks and identifies 9000 complexes, including 751 affected by SARS-CoV-2.

The paper introduces GENIX, a platform for identifying perturbation-relevant genes using gene association networks, applied to influenza vaccine responses in COVID-19 recovered patients.

This paper introduces PhenoPLIER, a method that maps gene-trait and drug associations into gene modules to reveal disease mechanisms and drug targets.

NetAn enhances gene annotation enrichment by integrating network topology, improving identification of disease-related annotations in Multiple Sclerosis.

Omix integrates and analyzes multi-omics data to generate pseudo-temporal profiles in Alzheimer's Disease using bulk proteomics and transcriptomics datasets.

RichPathR provides R functions for gene set enrichment analysis and visualization using EnrichR, facilitating exploratory analysis of gene lists in biological contexts.

This paper introduces ETNA, a joint embedding method for cross-species functional alignment using biological networks and sequence orthologs, showing improved preservation of gene functional relationships.

The paper integrates gene expression and protein-protein interaction data using tensor decomposition, improving classification and enrichment across 27 cancer types from TCGA.

metGWAS 1.0 statically evaluates independent metabolomics and GWAS studies to identify novel metabolite-gene associations using hypergeometric testing.

The paper introduces Cosinet, a tool for single-sample differential co-expression analysis in breast cancer, showing it can predict clinical outcomes like survival and recurrence.

This study identifies hub genes and enriched pathways in type 2 diabetes using bioinformatics analysis of T2D-associated genes.