Bioinformatics and Genomic Networks

BioKC provides a collaborative platform for curating and annotating systems biology models using SBML standards.

This study identifies disease mutations that perturb protein-protein interactions in the human 3D interactome, showing their association with cancer survival and drug response.

This paper identifies cancer subnetwork markers using a multi-objective approach combining differential gene expression and network topology, achieving better classification performance than existing methods.

leADS improves metabolic pathway inference by using active dataset subsampling to address class imbalance in training data.

Y2H-SCORES provides a statistical framework to identify protein-protein interactions from yeast-two-hybrid sequence data by evaluating enrichment, specificity, and in-frame selection.

Graphia is an open-source platform for graph-based analysis of complex biomedical data, offering visualization, transformation, and analysis tools for high-dimensional datasets.

The paper develops a network medicine framework to predict polyphenol therapeutic effects by analyzing the proximity of their protein targets to disease proteins in the human interactome.

KRSA predicts differential kinase activity from kinome array data by analyzing phosphorylation differences and comparing results to gene expression databases.

This study identifies differentially expressed genes and biological pathways in COVID-19 patients using bioinformatics analysis of gene expression data.

The paper extends Shannon's entropy to analyze taxa diversity and links protein mutual information to disease susceptibility.

This study analyzes SARS-CoV2's impact on host signaling pathways by examining codon usage patterns in host-viral protein interactions, identifying key pathways and proteins affected during COVID-19 pathogenesis.

The paper introduces a tool called DDH to predict gene functions by integrating gene essentiality, expression, and literature data, enabling hypothesis generation for thousands of human genes.

Graphein transforms bioinformatics data into machine learning-ready graph representations for geometric deep learning and network analysis.

This paper introduces Kirchhoff Knowledge Graphs (K-KG) to predict metastatic sites in breast cancer using multi-omics data, achieving high AUC and F1 scores.

The paper investigates cancer protein-protein interaction networks, finding they are scale-free and vulnerable to hub-directed combination therapy.

The paper models and analyzes host-pathogen interactions using a network-centric approach and demonstrates common mechanisms and differences in viral infections.

CoExp Web provides a web tool and software for analyzing and sharing co-expression network models.

PAMOGK integrates multi-omics data with pathway knowledge to cluster cancer patients, identifying subgroups with distinct survival and clinical features in kidney renal clear cell carcinoma.

Bio-JOIE learns joint representations of biological knowledge to predict protein interactions and cluster enzymes, outperforming existing methods in PPI type prediction.

The paper integrates ChIP-seq and gene expression data with gene regulatory networks to identify functional sub-pathways disrupted in lung cancer.

This study introduces ORN, a model that infers pathway activities and functional relationships in cancer using mutation and gene expression data, identifying survival-related pathways and novel mutation mechanisms.

PALS ranks significantly changing metabolite sets across conditions, showing robustness to missing data in metabolomics analyses.

The paper reconstructs signaling and regulatory networks for SARS-CoV-2 infection and identifies potential drug targets by integrating multi-omics data and experimental validation.

This paper introduces RINN, a deep learning model with L1 regularization to map genomic alterations to cancer signaling pathways, showing partial interpretability and predictive accuracy for differential expression.

The paper introduces a web framework for omics data analysis using R Shiny and Amazon Alexa, enabling interactive exploration and hypothesis generation for health and disease biomarkers.

The paper analyzes protein interactomes across 1800 species to show that biological networks develop macroscales associated with lower noise and higher resilience, particularly in eukaryotes.

Kaleidoscope is a web application that provides access to omics databases and tools for in silico hypothesis exploration, demonstrating its use with DISC1 gene analysis across multiple datasets.

The paper introduces OBIF, a systems model that identifies molecular drivers of synergy by integrating statistical and biological interactions from omics data.

This paper investigates gene set overlap as a cause of low specificity in over-representation analysis and shows a strong negative correlation between overlap and specificity.

This study uses genomic and proteomic data to analyze gene regulatory networks in breast cancer, identifying key genes and pathways involved in disease development and suggesting potential drug targets.