Bioinformatics and Genomic Networks

DeleteomeTools predicts gene function by analyzing yeast deletion strain transcriptomes, identifying co-functional genes and confirming novel functional relationships.

PHILHARMONIC infers protein interaction networks and functional organization in non-model organisms using deep learning and spectral clustering, validated in Drosophila and coral symbionts.

TRIPS identifies disease-associated transcription factors by integrating differential expression and network perturbation analysis in case-control data.

The paper evaluates challenges in predicting arenavirus-human PPIs, revealing issues with model generalization and proposing a framework for more reliable evaluation.

The paper introduces WGAND, a network-based anomaly detection method for weighted graphs, which identifies proteins with major roles in human tissues by analyzing protein interaction networks.

The paper analyzes function annotation biases in the human proteome over time, revealing persistent inequality in knowledge distribution and a gap between annotation and scientific interest.

This study identifies 26 hub genes shared across breast, lung, colorectal, and prostate cancers using RNA-seq data from TCGA, highlighting their potential as therapeutic targets and prognostic markers.

The paper develops a Nextflow workflow for reproducibly inferring regulatory networks from TCGA data, showing its application to colon cancer subtypes.

RWRtoolkit enables multi-omic network analysis using random walks on multiplex networks across any species, providing tools for topological analysis and statistical evaluation.

MTGCL identifies cancer driver genes using multi-task graph contrastive learning, improving performance by integrating multi-omics data and unlabeled node information.

EssSubgraph improves essential gene prediction by integrating networks and omics data, showing better performance and cross-species generalizability than existing methods.

The paper evaluates mutual gene pair interactions in human molecular networks, finding distinct expression patterns and connectivity differences between normal and cancer tissues.

MASCARA improves coexpression analysis in designed experiments by removing experimental variance to detect pathway-related coexpression patterns.

This study identifies lung cancer-specific driver modules using a clustering-based gene-gene interaction network analysis combined with machine learning.

This paper introduces GENN, a method for multi-omic interactome analysis that improves prediction of biomedical outcomes by learning salient associations in a low-dimensional space.

This study introduces CM2.0, a coevolution-based method to predict species-specific PPIs, revealing conserved and species-specific interactions in E. coli, S. enterica, and S. aureus.

This study benchmarks network inference algorithms for deriving metabolic relationships from lipidomic and metabolomic data, revealing challenges in interpretation despite some ability to distinguish metabolic states.

xOmicsShiny is an R Shiny application that enables cross-omics data analysis and pathway mapping with visualization and analytical tools.

MBCO PathNet integrates and visualizes functionally related pathways from transcriptomic and proteomic data using ontologies, providing pie charts, bar diagrams, heatmaps, and timelines.

This paper introduces llm2geneset, a framework using large language models to generate gene sets from input genes and context, improving overrepresentation analysis compared to human-curated databases.

GRPhIN characterizes graphlets in mixed physical and regulatory interaction networks, revealing conserved and stress-specific patterns across species.

The paper identifies differentially expressed genes (DEGs) in mouse tumor cells and analyzes their enrichment in biological pathways and fusion events.

The FunCoup Cytoscape App integrates FunCoup network data with Cytoscape for multi-species gene interaction analysis and visualization.

The paper introduces PlantaeViz, a web-based platform for integrating, visualizing, and analyzing multi-omics data across plant species, with a focus on transcriptomics and functional annotations.

This paper uses network analysis to identify key genes and patient subgroups in gliomas, revealing molecular heterogeneity and potential biomarkers.

GenePioneer identifies essential cancer genes and modules across 12 cancer types using a network-based model and provides a Python package for practical application.

The paper uses representation learning on plasma proteomic data to identify protein modules linked to disease and genetic variants, offering insights into biological processes and potential biomarkers.

This paper uses NMF to integrate PPI and Gene Ontology data to identify disease-associated gene modules and predict 3131 gene-disease associations.

This paper introduces a method using Large Language Models to perform deep functional profiling of gene sets, demonstrating its ability to uncover detailed immune functions and improve upon traditional annotation tools.

This paper introduces a method to guide dimensionality reduction and data fusion in multi-omics data by analyzing intrinsic dimensionality, showing improved performance in predicting survival outcomes.