Bioinformatics and Genomic 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 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.

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.

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.

This paper introduces SLWise, a deep-learning model for predicting synthetic lethal interactions in specific cellular contexts, and SLAD-CE for analyzing their mechanisms, showing improved accuracy over existing methods.

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 a method to identify key genes in disease networks by analyzing community-aware centrality, finding novel genes in prostate and cervical cancer.

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.

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.

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

This paper introduces Network Shape Intelligence (NSI) and shows it outperforms AlphaFold2 in predicting protein interactions using only local network information without training.

The paper quantifies how human gene functional connections change under environmental stress, revealing large-scale rewiring of genetic interactions across different conditions.

MELISSA improves protein function prediction by integrating functional labels during embedding, outperforming existing methods in yeast and human networks.

The paper identifies shared proteins and pathways between cardiovascular and cognitive diseases using protein interaction network analysis, highlighting oxidative and glycative stress mechanisms in vascular cognitive impairment.

This study applies collaborative network analysis to Huntington's disease transcriptomics data to identify enriched pathways and generate hypotheses about disease mechanisms.

EnrichMiner provides a web server to help researchers explore and refine functional enrichment analysis results by removing redundancy and selecting representative terms.

CRFE improves functional enrichment by using gene rankings to identify non-redundant, specific categories, outperforming existing methods in four RNA-seq datasets.

DENetwork identifies regulatory and signaling networks behind differentially-expressed genes by integrating DE and non-DE genes in a graphical model, enhancing traditional differential gene analysis.

The paper constructs the first large-scale gene coexpression network for peach and validates its ability to predict gene function related to fruit softening.

BrainProt 3.0 integrates multi-omics data to provide a resource for understanding human brain diseases and identifying therapeutic strategies.

IE-MOIF improves multi-omics data integration by enhancing information and using attention mechanisms, leading to better biomarker discovery and classification.

The paper predicts functional interactions of dark proteins with Reactome pathways using a random forest classifier and develops a web portal to visualize these interactions.

The paper evaluates the PredPrIn workflow for predicting interactomes across seven organisms, showing high precision and scalability, and compares cross-species interactions and biological processes.

The paper introduces a fuzzy semantic scoring function to assess protein binding affinity using Gene Ontology graphs and applies it to a human PPI network of 180 million potential interactions.