Bioinformatics and Genomic Networks

omicsViewer is a platform for visualizing and analyzing omics data, allowing interactive exploration and sharing of results with collaborators.

This study uses geometric network analysis of RNA-Seq data to cluster pediatric sarcoma tumors and identify therapeutic targets through unsupervised methods.

The paper introduces the AUTO-go framework to standardize and automate gene set enrichment analysis with reproducible visualizations in genomics research.

This paper identifies serine phosphorylation as a key post-translational modification in coronary artery disease and highlights GSK3B as a master regulator with significant predictive power.

The paper benchmarks machine learning models for predicting protein-protein interactions, showing that functional genomics models outperform sequence-based ones and that algorithm design has little impact on performance with functional genomic data.

This study analyzes NGS data to identify differentially expressed genes and hub genes associated with COVID-19 and its secondary complications, showing diagnostic potential.

This paper performs a meta-analysis of gene expression in colon rectal cancer to identify genes associated with tumorogenesis and potential therapeutic targets.

This paper introduces DeepSSC, a biphasic deep semi-supervised framework for multi-omics subtype classification and biomarker discovery, showing improved performance and novel biomarker identification.

The paper curates a database and develops an R package to identify gene-environment interactions by analyzing gene modules associated with environmental factors in disease.

This paper introduces Grandline, a graph convolutional neural network that integrates gene expression and protein interaction networks to predict phenotypes with high accuracy and interpretability.

SalmoNet2 provides an updated multi-layered network resource for 20 Salmonella strains, enhancing strain comparisons through protein-protein, transcriptional regulatory, and metabolic interactions.

MOPower is an R-shiny application that simulates multi-omics data and calculates power for association studies using various integration methods.

The paper identifies differentially expressed genes in NAFLD using NGS data and suggests potential hub genes as diagnostic biomarkers.

This paper constructs a multiple-layer interactome for SARS-CoV-2 by integrating human genes, miRNAs, and co-infecting microbes to identify potential drug targets.

This study identifies 15 key genes and 37 FDA-approved drugs potentially relevant to Multiple sclerosis, Schizophrenia, and Autism using bioinformatics tools and drug-gene interaction analysis.

This paper develops a core Tfr cell gene module using random forest to predict functional pathways, available for GSEA analysis across diverse datasets.

CAMO evaluates omics congruence between model organisms and humans by analyzing differential analysis, concordance, pathway investigations, and subnetwork detection.

SubcellulaRVis visualizes subcellular compartment enrichment of gene lists using a eukaryotic cell diagram, improving interpretation of protein localization from enrichment analyses.

The paper predicts SARS-CoV-2-human protein-protein interactions using multi-label classifiers, identifying key interactions and pathways involved in infection.

The paper analyzes CPTAC phosphoproteomics data to identify kinase activity differences between tumor and normal tissues across cancers and develops a web portal for querying kinase activity scores and associated data.

This paper introduces an entropy-based directed random walk method to improve pathway activity inference by incorporating gene interactions and topological importance, showing better sensitivity and accuracy in cancer classification.

This paper introduces a method to embed biological networks by considering topological similarity across layers, improving drug repositioning and target prediction accuracy.

OMAMO selects the best simple model organism for studying a biological process based on orthologous relationships between humans and species.

This study identifies common key genes and pathways between COVID-19 and COPD using a protein-protein interaction network approach.

This paper introduces a network-based deep learning framework to translate GWAS findings into biological insights and drug targets for Alzheimer's disease, identifying 156 putative risk genes.

The paper introduces a graph-theoretic method to analyze Alzheimer's disease-related genes and identifies three functional modules and 24 novel AD-related genes.

The paper introduces a multiplex network framework to identify immunological markers for Mild Moderate Alzheimer's Disease by analyzing autoantibodies and their functional relationships.

This paper identifies key ischemic stroke genes using computational systems biology methods, highlighting Cdkn1a, Nes, and Anxa2 as potential regulators.

The paper identifies common differentially expressed genes and hub genes between glioblastoma and Parkinson's disease using microarray data and bioinformatics tools.