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 identifies CDK1 and HSP90AA1 as key regulatory genes in NSCLC using network analysis of microarray data.

GeneCodis 4 extends functional enrichment analysis to regulatory elements like transcription factors, CpG sites, and miRNAs, with new databases and visualizations.

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

The paper defines functional network motifs by integrating genetic interaction data to identify biologically relevant feedback circuits in yeast.

TWO-SIGMA-G tests gene sets in scRNA-seq data by accounting for gene and cell correlations, showing improved power and error control in simulations and real datasets.

The paper introduces csdR, an R package for differential co-expression analysis, which improves performance and usability compared to existing implementations.

This paper introduces HyMM, a hybrid method for disease-gene prediction that integrates multiscale module structures, showing improved performance through cross-validation and independent testing.

The paper identifies robust functional gene modules in Escherichia coli using three-body correlations and community detection in gene co-expression networks.

MonaGO provides an interactive web-based visualization system for gene ontology enrichment analysis, offering dynamic clustering and customizable outputs to improve interpretation of results.

The paper introduces a method to assess network inference reliability by measuring functional assortativity in biological networks.

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.

The paper analyzes the statistical properties of the MetaCore protein-protein interaction network using Google matrix methods to identify activation and inhibition patterns in specific protein groups.

This study compares confound adjustment methods for gene co-expression network construction and finds that different methods significantly affect network architecture.

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

The paper introduces MUON, a framework for organizing, analyzing, and visualizing multimodal omics data, enabling flexible multi-omics analysis and interoperability with existing standards.

The paper introduces GCSC regression to identify gene sets enriched for disease heritability by leveraging gene co-regulation in TWAS data, finding immune-related associations in Alzheimer's disease.

The paper introduces a web-server for the DOMINO algorithm, which identifies active modules in gene networks and provides tools for analysis and interpretation.

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

The paper evaluates data processing steps for accurate gene co-expression prediction from RNA-seq data, finding that sample size, normalization, batch correction, and correlation measure significantly impact results.

FILER provides a large-scale, harmonized functional genomics data catalog with streamlined access to over 50,000 datasets and efficient querying capabilities.

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 uses transcriptogram analysis on SARS-CoV-1 microarray data to identify gene sets and their temporal relationship with viral titer during infection.

The paper identifies subgroups of preterm birth patients with shared protein interaction networks and pathway enrichments using sequencing and bioinformatic analysis.

The paper constructs gene co-expression networks for barley and sorghum using RNA-seq data and provides a database and visualization tool called PlantNexus.

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 study benchmarks 30 RNA-seq data processing workflows for constructing coexpression networks, finding that normalization methods significantly affect network accuracy.

NetMix2 unifies network propagation and altered subnetwork approaches to identify disease-related subnetworks, outperforming existing methods in simulated and real data.