Bioinformatics and Genomic Networks

MAGI-MS identifies genetic modules enriched in disease-relevant genes by integrating multiple seed genes associated with neurodevelopmental disorders.

NetREx provides a server for analyzing co-expression networks in rice under abiotic stress conditions to identify novel genes and tissue-specific interactions.

AGEpy is a Python package that transforms interpretable data into biological meaning through high-throughput analysis of pre-processed data using local or remote processing.

The paper introduces an R package called RCX that enables handling and converting biological networks in the Cytoscape Exchange format within R.

The paper constructs gene and function embeddings from literature-curated causal relationships to predict gene-function associations and disease drivers.

NORMA-2.0 is a web tool that uses layout strategies to group annotated network elements, improving visualization of network heterogeneity.

SynLethDB 2.0 is a database that compiles synthetic lethal gene pairs and related knowledge to aid in anticancer drug discovery, including over 16,000 new synthetic lethal interactions.

The paper develops a computational framework to analyze mutational hotspots in cancer genomes using a human 3D interactome network, identifying clinically relevant interactions and proteins.

Super.Complex detects protein complexes in interaction networks using supervised machine learning, identifying 1,028 complexes in human networks, including 234 linked to SARS-CoV-2.

This study uses Forman-Ricci curvature and network entropy to analyze gene networks and find correlations with cellular pluripotency and pathway robustness in stem cells and cancer cells.

The paper introduces JEBIN, a method to integrate multiple gene expression datasets into a low-dimensional gene representation, showing improved performance in co-expression network analysis and differential co-expression studies.

This paper introduces a method to detect dysregulated signaling pathways in cancer by comparing normal and cancerous cells using causal inference, showing improved performance over existing methods.

The paper identifies 272 muscle-specific differentially expressed genes in mice using microarray data and pathway analysis, providing potential candidates for studying muscle development mechanisms.

This paper introduces a cancer subtyping method using patient-specific gene regulatory networks, identifying more clinically meaningful subtypes than existing methods.

The paper introduces Biofactoid, a tool that crowdsources structured biological knowledge from authors, enabling efficient human-computer interaction and expanding existing pathway databases.

The paper evaluates how different node addition mechanisms affect the resilience of PPI networks across species, finding that gene-expression-based attachment best preserves resilience.

GeneTonic provides an R/Bioconductor package to streamline RNA-seq data interpretation by integrating differential expression and functional enrichment analyses into interactive visualizations and reports.

GraphOmics integrates multi-omics data through pathway mapping and interactive exploration to support hypothesis generation.

The paper tests AVA-Seq on a human PPI reference set, finding it recovers 43% of binary PPIs and identifies novel interactions.

This paper reviews the development of cancer systems biology models and their integration with patient data to support personalized therapeutics, highlighting the need for a unified multi-scale modeling pipeline.

The paper integrates protein imaging and interaction data to create a unified map of eukaryotic cell structure, revealing new subcellular systems and protein associations.

AutoGGN integrates multi-omics data and molecular interaction networks using graph convolutional neural networks, achieving better performance in cancer and single-cell classification tasks.

The paper presents a framework for integrating longitudinal multi-omics data to detect temporal relationships and interactions between omics layers, applied to cell cycle and aphid feeding studies.

This paper introduces Genetrank, a method to prioritize genes regulating drug pharmacodynamics using random walks on protein interaction networks, applied to single-cell TRAIL sensitivity data.

BioNERO provides an integrated R/Bioconductor package for streamlined biological network reconstruction, combining preprocessing, network inference, and functional analysis into a single workflow.

SBGNview provides a comprehensive tool for pathway analysis, visualization, and data integration across multiple species and databases.

This paper compares network propagation algorithms for multi-omics data integration and identifies optimal parameter settings for different research questions.

The paper introduces NECARE, a network-based method for predicting cancer-specific protein-protein interactions, revealing 1362 cancer hub genes and their association with cancer prognosis and treatment.

PaIRKAT improves testing power for pathway-phenotype associations by integrating graph structure into kernel regression, showing effectiveness in metabolomics data.

This paper introduces EDOHA, a method for estimating multiple interaction networks from high dimensional omics data, showing improved performance in identifying class-specific hubs.