Bioinformatics and Genomic Networks

The paper introduces COMPRESS-GI, an algorithm that efficiently selects informative genes for genetic interaction screens, enabling large-scale chemical genetic screens with high throughput.

The paper introduces a method that improves functional similarity computation by incorporating GO term enrichment, enhancing performance in predicting sequence homologies, gene expression correlations, and protein-protein interactions.

Inter-Tools aggregates protein interaction data from PSI-MITAB sources, creates species-specific interactomes, and provides basic analysis tools for interactome networks.

The paper constructs and analyzes RNA-seq-based gene co-expression networks in four plant species, identifying conserved and species-specific modules linked to biological functions and agronomic traits.

GeoDiver provides an online tool for differential gene expression and gene-set analysis on GEO datasets, with interactive visualizations and results sharing capabilities.

The paper builds a high-quality rice RNA-seq-based gene co-expression network using an ensemble method and demonstrates its utility for predicting gene function and regulation.

The paper develops a network approach to identify genetic vulnerabilities in cancer cell lines by analyzing shRNA screens and clustering coessential genes to discover context-specific essential genes and biomarkers.

This study shows that signaling pathway activities are better predictors of breast cancer prognosis than gene-level features and identify additional genes contributing to prognosis beyond pathway analysis.

The paper develops a statistical measure to assess the reproducibility of co-expression networks across tissues, finding full agreement between GTEX and BRAINEAC datasets.

GSA-Genie provides a web tool for gene set analysis with over 2.3 million gene sets and 16 statistical methods.

xMWAS integrates multiple omics datasets, identifies sub-networks, and performs differential network analysis across conditions.

Genoppi integrates proteomics and genetic datasets to identify biological relationships, offering a user-friendly web application for this purpose.

MEACA improves gene-set analysis by using mixed models to better handle gene correlations and reduce false positives while increasing statistical power.

The paper measures pleiotropy in yeast genes using genetic interaction networks, finding high-pleiotropy genes have broader functional influence and distinct characteristics compared to low-pleiotropy genes.

gene-cocite identifies and visualizes cocited genes from a list, providing statistical analysis and interactive paper links.

DepEst provides 11 dependency score estimators for gene network inference algorithms, available as an R package with parallel and serial execution capabilities.

CrosstalkNet is a web-based tool for visualizing and analyzing large-scale bipartite co-expression networks to study tumor-stromal cell interactions.

The paper introduces a Python library for enrichment analysis and visualization of gene sets and rankings using biological ontologies.

The paper develops a method to generate weighted gene annotations from Boolean annotations and gene expression data, showing improved resistance to gene-gene correlations and revealing biological insights.

The paper identifies genetic factors contributing to endurance heterogeneity in Xenopus tropicalis using gene and gene set analyses, revealing core genes, functional modules, and pathways related to muscle contraction and lipid metabolism.

The paper creates a gene set database for pathway analysis in mouse, enabling pathway analysis and meta-analysis of gene expression signatures.

Netpredictor predicts missing links in bipartite and unipartite networks using Random Walk with Restart and Network inference algorithms.

The paper introduces a web-based tool called GIRN for visualizing gene expression data through force-directed graphs with annotations and drug interactions.