Bioinformatics and Genomic Networks

A powerful framework for differential co-expression analysis of general risk factors

This paper introduces KDCA, a framework for differential co-expression analysis of general risk factors, showing improved power and reduced bias compared to existing methods.

Identifying potential therapeutic targets for T-cell acute lymphoblastic leukemia using malignant networks and topological analysis

This study identifies potential therapeutic targets for T-ALL by analyzing topological properties of protein-protein interaction networks and evaluating gene essentiality through network attack strategies.

CENTRA: Knowledge-Based Gene Contexuality Graphs Reveal Functional Master Regulators by Centrality and Fractality

CENTRA uses gene co-occurrence networks from literature to identify functional master regulators by analyzing centrality and fractality in topic-specific contexts.

From Genomics Alterations to Expression Dynamics: A Hierarchical Multi-Omics Integration Framework with gINTomics

gINTomics integrates multi-omics data hierarchically to model genomic alterations' impact on transcriptional processes, identifying regulatory mechanisms and biomarkers in ovarian cancer.

Beyond Single Algorithms: A Framework for Validating and Aggregating Active Modules in Genetic Interaction Networks

The paper introduces a framework to compare and aggregate outputs from multiple Active Module Identification algorithms in genetic interaction networks, finding that no single algorithm performs well across datasets and proposing methods to combine results.

CardioWAS Pathway-Based RNA-Seq and Genome Integration Platform for CVD Omics-Wide Associations

CARDIOWAS integrates RNA-seq and GWAS data to identify disease-specific pathways in cardiovascular diseases, enabling pathway-level analysis and variant-pathway associations.

NOODAI: A webserver for network-oriented multi-omics data analysis and integration pipeline

NOODAI is a webserver that integrates multi-omics data through network analysis to identify disease-relevant modules and provide analytical outputs.

GETgene-AI: A Framework for Prioritizing Actionable Cancer Drug Targets

GETgene-AI prioritizes actionable cancer drug targets by integrating network-based methods, machine learning, and literature analysis, achieving higher precision and efficiency than existing tools.

Distilling Direct Effects via Conditional Differential Gene Expression Analysis

The paper introduces conditional differential gene expression analysis to distinguish direct from mediated gene effects in RNA sequencing data, finding most differentially expressed genes are not directly causal.

XomicsDB: a database of paired multi-omics datasets for systems biology

XomicsDB curates and provides paired multi-omics datasets for model organisms to facilitate data integration and systems biology research.

A Co-essentiality Network of Cancer Driver Genes Better Prioritizes Anticancer Drugs

The paper constructs a co-essentiality network from CRISPR screens to prioritize anticancer targets and predict drug responses, outperforming conventional networks in drug repurposing.

multiDEGGs: a multi-omic differential network analysis package for biomarker discovery and predictive modeling

multiDEGGs performs differential network analysis across multi-omic data to identify biomarkers and improve predictive modeling in rheumatoid arthritis treatment response.

Computing the Human Interactome

The paper predicts 18,316 human protein-protein interactions with 90% precision using enhanced coevolutionary analysis and a new deep learning model.

ppIRIS: deep learning for proteome-wide prediction of bacterial protein-protein interactions

ppIRIS predicts bacterial protein-protein interactions using deep learning, achieving high accuracy and enabling proteome-wide screening, with applications in identifying virulence pathways and host-pathogen interactions.

Conservation of the genetic interaction network between two yeast species

This study quantifies genetic interaction conservation between two yeast species, finding significant conservation among functionally related genes but reduced conservation due to gene duplication.

NeKo: a tool for automatic network construction from prior knowledge

NeKo automates biological network construction by integrating molecular interactions from databases, enabling efficient network building for research applications.

Unraveling cooperative and competitive interactions within protein triplets in the human interactome

The paper develops a computational framework to predict cooperative and competitive interactions among protein triplets in the human interactome using hyperbolic embeddings and machine learning.

Evaluating Multiomics Integration Architectures for Training With Structured Missingness

This study evaluates integration strategies for multimodal bioinformatics data with structured missingness, finding late integration and logistic regression models perform best in breast cancer subtyping.

MORGaN: self-supervised multi-relational graph learning for drug target discovery

MORGaN is a self-supervised framework for drug target discovery that outperforms existing models by leveraging multi-omic, multi-relation gene networks and provides interpretable results.

MPAC: a computational framework for inferring pathway activities from multi-omic data

MPAC infers pathway activities from multi-omic data to identify patient subgroups and clinically relevant proteins, demonstrating improved results over single-omic analyses in head and neck cancer.

Integrating computational protein structure predictions and genetic dependencies yields an atlas of human multi-protein complexes (AHMPC)

The paper constructs an atlas of human multi-protein complexes by integrating computational structure predictions with genetic dependency data, identifying 354 high-confidence complexes including novel ones.

Simulated metabolic profiles reveal biases in pathway analysis methods

This study simulates metabolic profiles to evaluate pathway analysis methods, revealing biases when applied to metabolomics data.

Pathway impact analysis (PIS) for robust and comprehensive interpretation of differentially expressed genes (DEGs)

The paper introduces PIS, a method to improve DEG selection by optimizing thresholds for pathway enrichment, showing better recovery of true DEGs and stability across datasets.

ProteinWeaver: A Webtool to Visualize Ontology-Annotated Protein Networks

ProteinWeaver visualizes protein networks in five model organisms, contextualizing proteins within biological processes using Gene Ontology terms and identifying network motifs and distances.

Hyperbolic Wavelet Interaction Network (HyWinNet): Multi-Scale Geometric Learning for Interpretable Protein-Protein Interaction Prediction

HyWinNet predicts protein-protein interactions by integrating hyperbolic geometry and wavelet transforms to model multi-scale biological networks, outperforming existing methods in recovering known interactions and predicting functional sites.

SPACE: STRING proteins as complementary embeddings

The paper develops cross-species protein embeddings using network data and shows they improve subcellular localization and function prediction when combined with sequence embeddings.

Multi-omics time-series analysis in microbiome research: a systematic review

This paper reviews data science methods for longitudinal multi-omics in microbiome research, highlighting the need for integrated approaches and the lack of widely adopted methods.

A-LAVA: Detecting impact of germline variants on metabolic pathways in cancer genomes

A-LAVA identifies germline variants affecting metabolic pathways in cancer genomes through SNP, gene, and pathway-level analysis, improving accuracy by correcting for overlapping gene sets and reducing false positives.

Patient stratification reveals the molecular basis of disease comorbidities

The paper develops a method to generate a disease network using RNA-seq data, revealing molecular mechanisms behind comorbidities and identifying new associations through patient stratification.

EnrichMET: R Package for Integrated Pathway and Network Analysis for Metabolomics

EnrichMET provides an R package for fast and reproducible pathway enrichment analysis in metabolomics, supporting multiple methods and offering both command-line and Shiny interfaces.