Bioinformatics and Genomic Networks

NetMix improves subnetwork identification by reducing statistical bias in altered subnetwork estimation, outperforming existing methods on simulated and real data.

This study benchmarks nine joint multi-omics dimensionality reduction methods using simulated and real cancer data, finding intNMF excels in clustering and MCIA shows consistent performance across contexts.

The paper introduces DOMINO, a novel algorithm for identifying active modules in omics data, which outperforms existing methods by reducing false calls through permutation-based significance evaluation.

COSMOS integrates multi-omics data with prior knowledge to generate mechanistic hypotheses about biological processes in renal cell carcinoma.

The paper proposes GCN-based methods to detect protein complexes from PPI networks, showing improved performance over existing methods.

This study identifies key genes and regulatory mechanisms in acute myocardial infarction using bulk and single-cell RNA-seq data, highlighting PLAUR's role in inter-cellular signaling.

MendelVar prioritizes genes at GWAS loci by integrating Mendelian disease gene associations and phenotype ontologies to enhance gene annotation and drug discovery.

This study identifies gene networks and pathways associated with plasma lipid traits using multi-tissue multi-omics analysis, revealing both known and novel regulators of lipid metabolism.

The paper reanalyzes high-throughput chemical genomics data for E. coli K-12 to assess phenotypic profile similarity metrics and their ability to infer gene function.

The paper introduces ASACO, a tool for co-expression analysis of genes across transcriptomics data, identifies host factors involved in SARS-CoV infection, and validates findings against high-throughput screens.

NECo is a node embedding algorithm for multiplex heterogeneous networks that outperforms existing methods in classifying hypertension-related genes using rat disease models.

BiCoN identifies patient subgroups and functional gene subnetworks by integrating network constraints with biclustering of omics data.

DriveWays identifies overlapping cancer driver pathways by solving an NP-hard optimization problem, outperforming existing methods on TCGA data.

The paper introduces MultiPaths, a Python framework for analyzing multi-layer biological networks using diffusion algorithms, showing improved predictive performance through integrated multi-omics data.

The paper investigates how genomic alterations affect molecular networks in prostate cancer by integrating genomic, transcriptomic, and proteomic data from 105 samples, identifying a sub-network predictive of tumor aggressiveness and recurrence.

XGSEA predicts gene set enrichment significance in one species using gene expression from another species through domain adaptation and regression.

APODHIN integrates multi-omics data into a human interactome network to identify key molecular players and their interconnections in cancer, providing tools for analysis and visualization.

The paper aligns SARS-CoV-1 and SARS-CoV-2 virus-host protein-protein interaction networks using integer linear programming to identify functionally related human proteins involved in viral infection.

The paper introduces a model using knowledge graphs to predict kinase-substrate networks, achieving higher coverage and accuracy than existing methods through network-based learning.

The paper applies Google matrix analysis to the SIGNOR protein-protein interaction network to identify effective interactions and dominant activation/inhibition functions between proteins.

multiGSEA combines GSEA-based pathway enrichment across multiple omics layers using 8 pathway databases and supports 11 organisms.

This study uses genetic associations from GWAS and network propagation to identify drug targets enriched for clinical success.

The paper investigates why pathway analysis methods perform better than expected, finding that their effectiveness stems from overlaps with transcription factor regulons rather than accurate pathway activity inference.

The paper identifies genes and pathways shared between SARS-CoV-2 infection and COVID-19 comorbidities using bioinformatics tools.

The paper analyzes the robustness of multilayer biological networks by integrating gene regulatory, protein-protein interaction, and metabolic data to assess how perturbations affect system stability.

VolcaNoseR is a web tool for creating, exploring, labeling, and sharing volcano plots from genome- and proteome-wide screens.

The paper evaluates how incomplete PPI networks contribute to functional annotation across species, showing improved performance with additional data sources like text mining.

MOVICS integrates and visualizes multi-omics data for cancer subtyping, offering clustering algorithms and downstream analyses with customizable visualizations.

IMIX integrates multiple omics data types using a multivariate mixture model to improve FDR control and reduce misclassification rates compared to single-omics analyses.

The paper compares transcriptome and translatome networks in cardiac remodeling to identify differences in gene regulation and functional modules.