Bioinformatics and Genomic Networks

Genoppi integrates proteomic and genetic data to identify cell-type-specific interactions and their role in disease.

The paper introduces a method to estimate trait-specific co-expression networks by integrating parametric interaction models with co-expression networks, demonstrating its application in acute myeloid leukemia.

The paper connects SARS-CoV-2 viral proteins to host cell functions and diseases using a knowledge graph and machine learning, identifying potential drug targets for COVID-19.

This paper introduces structural gene expression signatures (sGES) derived from protein structure features, showing their utility in identifying robust biological signatures across datasets and experimental platforms.

The paper creates two large-scale cell-line-specific human interactomes using affinity-purification mass spectrometry and identifies shared and cell-specific protein interactions.

The paper introduces Inter-N-of-1, a method to detect biomechanism differences in small cohorts by analyzing single-subject transcriptome data, showing high precision and recall in simulations and a clinical dataset.

CoffeeProt is an open-source tool that integrates proteomics, genetics, and phenotypic data to identify functionally relevant genetic associations in mouse and human populations.

Quickomics is an R Shiny tool for interactive exploration of omics data analysis results, offering customizable modules and visualization options.

multiSLIDE is a web tool for visualizing interconnected molecular features in multi-omics data through keyword searches and customizable heatmaps.

AKLIMATE predicts cancer phenotypes by integrating multi-omic data and pathway knowledge, outperforming existing methods in tasks like microsatellite instability prediction and survival analysis.

This paper develops a statistical method to identify gene co-essentiality, creating a genome-wide functional module almanac that predicts functions for 102 uncharacterized genes and validates key findings.

CytoTalk constructs cell type-specific signal transduction networks from single-cell RNA-Seq data and validates them using spatial transcriptomics and perturbation experiments.

Arena3Dweb provides an interactive 3D web tool for visualizing multilayered networks, supporting drug repurposing and GPCR signaling pathway analysis.

The paper performs secondary transcriptomic analysis of SARS-CoV-2 infection models to identify conserved differentially expressed genes and their associations with COVID-19-related traits and pathways.

GO-Figure! is an open-source Python tool that generates semantic similarity scatterplots of GO term lists to aid in visualizing and comparing functional enrichment results.

This paper predicts synthetic lethal gene pairs using a similarity fusion approach with k-NN classification, showing effectiveness and practicality.

The paper introduces a probability landscape approach to analyze gene network changes during yeast replicative aging, revealing distinct roles for essential and hub proteins.

This paper predicts SARS-CoV-2 virus-host interactions using a network-based machine learning method, identifying five potential infection targets and eight protein-level interactions.

iNetModels 2.0 provides an interactive platform for visualizing and exploring multi-omics associations in human health and disease.

This paper introduces network filters to reduce noise in large-scale biological data, improving accuracy in predicting protein expression changes in human tissues.

The paper identifies viral-mediated pathogenic mechanisms in multiple sclerosis using network-based analysis of virus-host and disease-related protein interactions.

TRIAGE integrates pathway and network methods to optimize gene hit selection from high-throughput assays, improving consistency across genome-wide studies.

KiMONo integrates multi-omics data to infer networks of molecular associations, demonstrating robustness to noise and identifying disease-specific features in cancer and depression datasets.

KnetMiner integrates literature and data to find links between traits and genes across species, providing tools for hypothesis generation and gene discovery.

This paper introduces a multiscale interactome framework to explain drug mechanisms by integrating drugs, diseases, proteins, and functions, showing drugs often target functionally related proteins rather than disease-associated ones.

The paper compares six network methods using GWAS data from the GENESIS study to identify breast cancer susceptibility genes, finding that network approaches improve results over standard GWAS.

NOGEA identifies master genes driving disease initiation and comorbidity, predicts drug-disease associations, and validates new drug candidates for pancreatic cancer.

GAUSS identifies gene-set-phenotype associations using GWAS summary statistics, improving power and interpretability in UK Biobank data.

The paper introduces GMSCA, a tool to enhance gene multifunctionality predictions in bulk-tissue co-expression networks, revealing new roles for genes like SNCA in oligodendrocytes.

The paper evaluates the reusability and composability of manually-built RAS-RAF-MEK-ERK signaling pathway maps from three databases to improve disease model assembly.