Bioinformatics and Genomic Networks

This paper introduces mantis-ml, a stochastic semi-supervised learning framework to prioritize genes from genomic screens by integrating gene annotations, achieving an average AUC of 0.85.

PhenoGeneRanker prioritizes genes using a network propagation method on multi-omics data, showing enriched cold tolerance-related genes at the top.

The paper evaluates adjusted spectral clustering methods for detecting gene clusters in eQTL data, considering regression errors in network construction.

TEMPO detects pathway-specific temporal dysregulation in disease by analyzing combined temporal data from multiple individuals.

The paper introduces Coevolution Similarity Networks (CSNs) to analyze enzyme families by clustering based on coevolution patterns, showing higher predictive power for substrate specificity annotation than Sequence Similarity Networks.

NGSEA improves gene set enrichment analysis by incorporating functional networks, outperforming GSEA in pathway identification and drug repositioning.

The paper uses network analysis to compare immune systems of wild and laboratory mice, finding similarities and differences in their immunological networks.

The paper introduces Bio2BEL, a set of Python packages that convert multi-modal biological data into Biological Expression Language for integration and analysis.

This paper introduces GeneSet MAPR, a supervised learning algorithm for gene set analysis that leverages network patterns to improve identification of genes related to a given gene set, outperforming existing methods.

The paper reviews the evolution of disease networks and evaluates their functional units to inform drug repurposing and prediction model development.

ExpressWeb is a web tool that clusters and visualizes expression data, enabling gene network extraction and co-expression analysis through heatmaps, graphs, and networks.

The paper predicts host-pathogen interactions by integrating phenotypic, functional, and taxonomic knowledge using an ontology-based machine learning method.

pathwayPCA is an R package that performs integrative pathway analysis with modern PCA methods and gene selection, enabling multi-omics data analysis and sample-specific pathway activity estimation.

The paper presents HuRI, a comprehensive human protein-protein interaction map, and demonstrates its utility in understanding cellular function and disease mechanisms.

The paper examines plasmid-chromosome gene interactions in E. coli to identify a new invariant of bacterial protein-protein interaction networks related to co-evolution.

Malachite automates gene enrichment meta-analyses across multiple datasets using ToppGene, identifying xenobiotic stimulus and NADP activity as enriched processes in smoking-related gene expression data.

This study uses spectral graph theory and complexity measures to analyze molecular networks, finding that graph energy and cyclomatic complexity can specify network motifs effectively.

The paper introduces SCINET, a framework to reconstruct cell-type-specific interactomes from single-cell data, revealing disease-specific gene networks and cell-type convergence in brain disorders.

The paper shows treeClust, an unsupervised machine-learning algorithm, outperforms correlation-based methods in detecting protein co-regulation in proteomics data due to its robustness to outliers and focus on close linear relationships.

This paper introduces a tensor decomposition algorithm to analyze higher order gene correlations in yeast, revealing new insights into inter- and intra-pathway signaling structures.

This study identifies differentially expressed genes in hepatocellular carcinoma using bioinformatic analysis of gene expression data and finds enriched pathways and hub genes associated with cancer progression.

This study develops a signaling network model to identify loop sub-structures in PPI networks that predict drug resistance in medulloblastoma subtypes.

The paper evaluates 375 PPI databases, compares 16 for human PPIs, and recommends STRING and hPRINT for comprehensive PPI coverage.

The paper identifies proteins that control multiple molecular pathways and shows their enrichment in regulatory genes, disease genes, and drug targets.

csuWGCNA combines signed and unsigned WGCNA to better detect negative correlations, improving module detection and biological interpretation in gene regulation.

The paper introduces an R package for analyzing intra-species and inter-species protein-protein interactions in Arabidopsis thaliana, with features for comparing centrality measurements and orthologous proteins.

AligNet aligns protein-protein interaction networks by balancing structural matching and function conservation, improving efficiency over existing methods.

The paper analyzes thermodynamic measures of human brain development using Gibbs free energy from protein-protein interaction networks across age and sex.

The paper analyzes thermodynamic measures of human brain development by examining Gibbs free energy in protein-protein interaction networks across age and sex.

PGCN jointly trains graph convolutional network embeddings and association prediction on a heterogeneous gene-disease network to prioritize candidate disease genes and recover missing associations in OMIM.