Bioinformatics and Genomic Networks

The paper identifies human linear motifs by analyzing viral protein interactions, finding 8,400 motif instances with 105 supported by multiple filters.

PathGPS uses GWAS summary data to identify genetic architecture by separating genetic components from environmental factors and finding gene-trait clusters associated with biological pathways.

BioNE integrates network embeddings for supervised learning tasks, enabling comparison of different embedding methods in biological contexts.

This paper introduces HAllA, a method for discovering associations in multi-omic datasets, showing it outperforms existing approaches and identifies new biological relationships.

The paper develops a MINERVA plugin suite for integrating multi-omics data and performing in silico perturbation experiments on disease maps using RNA-seq datasets from the Atlas of Inflammation Resolution.

This paper uses biclustering to analyze SARS-CoV-2 infection's transcriptional responses, finding more enriched GO terms and KEGG pathways than machine learning approaches.

The paper develops NetKSA, a network-based machine learning framework to predict kinase-substrate associations, outperforming existing methods and enabling annotation of phosphosites targeted by less-studied kinases.

This study relaxes GWAS significance thresholds and uses GRIN to identify additional genes linked to suicidal behavior, which replicate across cohorts and show stronger biological connections.

DeRegNet identifies maximally deregulated subnetworks from multi-omics data using a novel algorithm, outperforming existing methods and identifying biologically relevant subgraphs in liver cancer.

NETSCOPE infers and analyzes biological networks using information-theoretic methods, validated on synthetic and real data across multiple domains.

The paper models COVID-19 disease biology to identify drug treatment candidates by analyzing protein-protein interactions and disease-associated genes.

The paper semantifies and harmonizes biological pathway diagrams from WikiPathways to enable semantic querying and integration with linked data.

MetaRelSubNetVis enables group-wise network visualizations with patient data and facilitates reliable sharing of visualizations between collaborators.

The paper introduces R-based tools for reproducible integration, exploration, and visualization of biological networks, with available code and documentation.

The paper updates the mitoXplorer platform to integrate mitochondrial expression dynamics with cellular context, introducing new functions for network analysis and transcription factor enrichment.

ProFeatMap provides a customizable 2D visualization tool for protein features, enabling users to explore domain and modification information from a list of proteins.

The paper evaluates factors affecting protein interaction prediction using phylogenetic profiles in eukaryotes, finding that orthologous group and interactome selection have greater impact than genome selection.

The paper identifies cancer-relevant genes by analyzing their proximity to core cancer genes in protein-protein interaction networks, finding that genes closer to cancer genes show greater functional importance in cancer biology.

The paper constructs disease-specific cytokine profiles by linking disease genes to immune responses using protein-protein interaction networks and association scores.

The paper compares SARS-CoV-2 and H1N1 influenza protein-protein interaction networks using topological features to identify essential proteins and assess network properties.

The paper integrates gene expression and cell shape imaging data to identify phenotype-specific gene networks and signaling pathways involved in breast cancer cell morphology.

ALGAEFUN with MARACAS provides tools for analyzing RNA-seq and ChIP-seq data from microalgae to extract biological insights through functional enrichment and differential expression analysis.

The paper uses pathway activity estimated via singular value decomposition to predict survival time in breast cancer patients, finding it more prognostic than individual transcripts.

Essential Regression infers causal latent factors from multi-omic human datasets, outperforming existing methods in prediction performance and enabling causal inferences.

The paper identifies drug targets by analyzing breakdown points in protein-protein interaction networks of cancer fusions, showing improved patient survival in specific cancer subtypes.

AutoCoEv predicts inter-protein co-evolution from large datasets, identifying novel interactions and functional clusters among 324 B lymphocyte lipid raft proteins.

The paper introduces REMI, a graph-based method to reconstruct cell-cell interactions in lung adenocarcinoma by accounting for ligand-receptor dependencies, and validates its findings experimentally.

TPAC improves cancer prognosis and tumor stage prediction by analyzing pathway dysregulation in tumor transcriptomics data using normal tissue-specific gene information.

UniFuncNet integrates data from multiple biological databases to create a flexible and comprehensive network for omics data analysis.