Bioinformatics and Genomic Networks

Ensemble-GNN: federated ensemble learning with graph neural networks for disease module discovery and classification

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

hipFG: High-throughput harmonization and integration pipeline for functional genomics data

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

An in-depth comparison of linear and non-linear joint embedding methods for bulk and single-cell multi-omics

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: Back to the Basics of Gene Enrichment

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

Hypergraphs and centrality measures identifying keyfeatures in gene expression data

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

Graphlet-based hyperbolic embeddings capture evolutionary dynamics in genetic networks

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: COntext-specific Network Embedding via Contextualized Graph Attention

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

MOAL: Multi-Omic Analysis at Lab.A simplified methodology workflow to make reproducible omic bioanalysis.

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

CoRegNet: Unraveling Gene Co-regulation Networks from Public RNA-Seq Repositories Using a Beta-Binomial Statistical Model

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: An R-package for advanced RNA-seq data exploration, disease stratification and therapeutic targeting

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

Rummagene: Mining Gene Sets from Supporting Materials of PMC Publications

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

Broad functional profiling of fission yeast proteins using phenomics and machine learning

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

Dimensionality reduction methods for extracting functional networks from large-scale CRISPR screens

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

Extending protein interaction networks using proteoforms and small molecules

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

SENSE-PPI reconstructs protein-protein interactions of various complexities, within, across, and between species, with sequence-based evolutionary scale modeling and deep learning

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

GeneSetR: A web server for gene set analysis based on genome-wide Perturb-Seq data

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: Computationally Constructing A Comprehensive Atlas of Human Protein Complexes

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.

Comparative Analysis of Association Networks Using Single-Cell RNA Sequencing Data Reveals Perturbation-Relevant Gene Signatures

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.

Projecting genetic associations through gene expression patterns highlights disease etiology and drug mechanisms

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

NetAn: A Python Toolbox for Network-based Gene Annotation Enrichment

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

Omix: A Multi-Omics Integration Pipeline

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

RichPathR: a gene set enrichment analysis and visualization tool

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

Joint embedding of biological networks for cross-species functional alignment

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.

Integrated analysis of gene expression and protein-protein interaction with tensor decomposition

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: An R workflow for network-driven over-representation analysis between independent metabolomic and meta-genome wide association studies

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

Patient-Specific Analysis of Co-expression Networks for Predicting Clinical Outcomes in Breast Cancer

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.

In-silico analysis reveals hub genes and enriched pathways in Type 2 Diabetes Mellitus

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

Integration of Thermal Proteome Profiling with phosphoproteomic and transcriptomic data via mechanistic network models decodes the molecular response to PARP inhibition

The paper integrates Thermal Proteome Profiling with phosphoproteomic and transcriptomic data using network models to uncover molecular responses to PARP inhibition in ovarian cancer.

Biochemical Pathways Represented by Gene Ontology Causal Activity Models Identify Distinct Phenotypes Resulting from Mutations in Pathways

The paper converts human GO-CAMs to mouse GO-CAMs to link gene pathway perturbations with phenotypes, showing distinct outcomes from glycolysis and gluconeogenesis gene mutations.

Shared and distinct pathways and networks genetically linked to coronary artery disease between human and mouse

This study identifies shared and distinct genetic pathways between human and mouse in coronary artery disease using multi-omics data and GWAS, showing over 75% shared pathways.