Bioinformatics and Genomic Networks

The paper identifies sex differences in lung adenocarcinoma by integrating multi-omics data to discover sex-biased signaling pathways and potential sex-specific therapies.

PatientProfiler integrates multi-omic data to generate patient-specific mechanistic models and prognostic biomarkers for breast cancer.

FISHNET identifies significant genes missed by standard methods by analyzing gene-level P-values with network and function-based approaches, showing improved replication rates in cardiovascular traits.

This study benchmarks 38 gene embedding methods across three functional prediction tasks, finding literature-based embeddings perform best overall, while sequence-based models excel in genetic interactions.

PathX-CNN improves survival prediction and pathway analysis in glioblastoma by integrating multi-omics data with sub-cohort-specific principal components and SHAP explanations.

GEM-Net constructs multi-modal gene networks using harmonized omics data from the Long Life Family Study, revealing a microbiome-immune axis linked to metabolic health.

The paper constructs a pan-cancer co-expression network integrating RNA and protein data to identify immune-related pathways and prognostic modules across cancers.

LDAK-PBAT detects gene pathways associated with complex traits using GWAS summary statistics, outperforming existing methods in sensitivity and specificity.

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.

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

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.

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 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 DANSy, a method converting proteins into linguistic networks using domain architecture to uncover functional relationships in proteomes, showing it complements traditional interaction networks.

This paper introduces GNA, a framework that uses network modeling on GWAS data to identify genetic architecture and trait-specific biology.

The paper introduces the mitology R package to specifically analyze mitochondrial activity using transcriptomic data, including single-cell and spatial transcriptomics.

DyNDG identifies leukemia-related genes using a dynamic network model integrating time-series differential genes, showing improved accuracy over existing methods.

This paper links Gene Set Enrichment Analysis to the Lorenz curve from economics, showing their statistical relationship and conceptual parallels.

This study identifies potential hub genes and therapeutic targets in colorectal cancer using integrated bioinformatics analyses of gene expression and functional data.

The paper identifies common molecular signatures between myocardial infarction and lung cancer subtypes, proposes potential therapeutic drugs, and demonstrates predictive models for MI classification.

The paper evaluates machine learning models for predicting retroviral-human protein-protein interactions using public databases and feature importance analysis.

This study uses network biology methods to prioritize potential Alzheimer's disease-associated genes by integrating multimodal datasets and applying PhenoGeneRanker.

The paper introduces an R package, gen3DNet, for generating 3D network models from two correlated data objects with shared factors, using non-negative matrix factorization and partial least squares regression.

The paper shows that genetic interactions correlate with protein-protein interactions in yeast and human genomes, and that GI and PPI networks share overlapping topological features.

The paper develops a method to create personalized regulatory networks for cancer patients using PDX models, integrating gene expression data and genetic algorithms with GCNs to predict treatment outcomes.

The paper refines gene expression signatures using multi-omics data to improve enrichment analysis by addressing lack of coordinate expression in gene sets.

The paper develops an AI model to identify core and context-specific essential genes using CRISPR and omics data, finding 3,213 pan-cancer CEGs with 60% overlap to prior studies.