Bioinformatics and Genomic Networks

The paper identifies cortical areas with enriched disease-associated genes using transcriptome-based parcellation and GWAS data, revealing potential roles in psychiatric and neurological disorders.

XlinkCyNET is a Cytoscape plugin for visualizing protein interaction networks derived from cross-linking mass spectrometry data.

TSUNAMI is a tool for mining gene co-expression networks and performing downstream analysis, offering features like web interface, database access, and visualization.

OmicLoupe enables interactive exploration and statistical comparison of multiple omic datasets to identify technical issues and biological patterns.

The paper introduces a conceptual framework and bioinformatics pipeline for multi-omics data integration, validated on TCGA datasets to enhance feature identification compared to single-omics analysis.

MegaGO assesses functional similarity between meta-omics datasets using semantic GO term similarity, offering fast and scalable analysis.

The paper integrates intra- and intercellular signaling knowledge into a database for multicellular omics analysis, demonstrating its application to SARS-CoV-2 infection and ulcerative colitis.

DeepViral improves prediction of virus-host protein interactions by incorporating disease phenotypes and protein sequences.

The paper provides R scripts for analyzing and visualizing blood transcriptional module repertoires, demonstrating their application in septic patients' transcriptome data.

This paper develops a method to tune pathway reconstruction algorithms to avoid biologically implausible predictions by using graphlet decomposition metrics and pathway databases.

The paper introduces 'persistence' as a metric combining protein turnover and RNA expression to improve functional insights from transcriptomics data, showing its effectiveness in schizophrenia datasets.

This paper introduces RoKAI, a network-based framework that improves kinase activity inference by integrating functional information, showing enhanced accuracy and robustness in identifying dysregulated kinases in diseases.

The paper examines how gene conservation, essentiality, and function influence connectivity in bacterial protein-protein interaction networks.

This paper introduces GCATSL, a graph contextualized attention network for predicting synthetic lethality in human cancers, outperforming existing methods on multiple datasets.

BetweenNet identifies cancer driver genes by integrating genomic data with protein-protein interaction networks using betweenness centrality and random walks, outperforming existing methods in recovering known cancer genes.

NetQuilt improves protein function prediction across species by integrating sequence and network data from multiple organisms.

The paper develops a web resource to identify potential targets for genetically modified crops by analyzing co-expression networks and orthology of proteins of unknown function in plant genomics data.

The paper introduces SBGN bricks and their ontology to provide structured semantics for recurring molecular network concepts, enabling better annotation and construction of molecular maps.

ViralLink is a systems biology workflow that reconstructs and analyzes intracellular signaling networks affected by viruses, using transcriptomics data to identify key pathways and proteins in the cellular response to infection.

The paper introduces iDRW, a multi-layered network-based pathway activity inference method, showing improved outcome prediction in urologic cancers compared to existing methods.

GeneWalk identifies gene functions relevant to a biological context using network representation learning and validates it on RNA-seq and NET-seq data from human and mouse samples.

This study constructs and analyzes a protein-protein interaction network for NAFLD, identifying 77 candidate proteins and validating some through gene expression analysis.

This study compares psychiatric disorders using pathway, cell-type, drug-target, and chromosomal analyses to identify common and distinct features across an unbiased cluster of disorders.

The paper curates and expands the yeast complexome, showing increased genetic interactions and functional links among complex members.

The paper presents a web interface for a mechanistic model of the SARS-CoV-2 disease map to infer gene expression-related activities and study intervention effects.

The paper introduces signed distance correlation to build gene coexpression networks from gene expression data, showing improved stability and biological relevance compared to Pearson or Spearman correlations.

This paper introduces CRANE, a method to assess the robustness of network structure changes by generating ensembles of gene regulatory networks and evaluating the significance of observed community shifts.

The paper investigates the connectivity between transcriptional regulatory and protein-protein interaction networks across five eukaryotic species, revealing conserved design principles and their impact on network robustness.

The paper introduces NETPAGE, a network propagation method to analyze rare mutations in Alzheimer's disease, identifying tissue-specific hub genes and predicting disease status.

MiBiOmics provides a web-based tool for multi-omics data integration and analysis, enabling visualization, ordination, and network inference to identify biomarkers.