Bioinformatics and Genomic Networks

The paper introduces CORNETO, a unified framework for knowledge-driven network inference from omics data, improving performance on biological tasks using simulated and real data.

The paper improves protein interaction prediction by addressing incomplete features and proteome coverage, identifying cell line-specific complexes.

The paper evaluates methods for inferring kinase-driven signaling pathways using phosphoproteomics data and kinase-substrate networks, finding that literature-curated networks with network propagation best recover ground truth interactions.

The paper introduces todenE, a clustering method combining topology and density-based approaches to group PAGs into Super-PAGs, improving functional representation in genomics.

SOFA identifies novel sources of variation in multi-omic data by incorporating known covariates and focusing on unexplained factors.

The paper proposes a deep learning model to predict human essential genes using weighted PPI networks and achieves better performance than centrality-based methods.

The paper introduces a software tool for interpreting and visualizing KEGG pathway and Gene Ontology functional analysis, offering interactive networks, hierarchical analysis, and efficient processing.

GAUDI integrates multi-omics data using UMAP embeddings and density-based clustering to uncover non-linear relationships and identify interpretable latent factors across datasets.

This study uses scRNA-seq data with machine learning to predict plant metabolic pathway genes, finding higher accuracy compared to bulk RNA-seq and suggesting more benchmark genes improve predictions.

This study develops a method to predict protein-protein direct interactions and stable complexes using co-expression and co-evolution data, achieving high AUROC scores and better performance than existing tools.

This paper introduces a knowledge graph to prioritize cancer mutations for targeted therapy by integrating clinical, functional, and structural data.

The paper shows that gene loss in cancer is often compensated by paralog protein abundance changes, stabilizing protein-protein interaction networks and creating therapeutic vulnerabilities.

The paper customizes gene set collections for single-cell RNA-seq by incorporating cell type-specific gene expression data to improve gene set testing power and interpretability.

BioPAX-Explorer simplifies querying and analyzing BioPAX biological pathway data by providing an object-oriented Python framework with query capabilities and tools for data extraction and workflow development.

G2PDeep-v2 is a web-based deep learning platform for phenotype prediction and biomarker discovery using multi-omics data across organisms, offering model training and visualization tools.

This paper develops a graph curvature-based pipeline to identify immune checkpoint response biomarkers from tumor RNA-sequencing data using PPI networks and transfer learning.

This paper introduces a statistical method using Gaussian Graphical Models to construct gene networks in bacteria, demonstrating its effectiveness in uncovering gene functions and interactions across multiple bacterial species.

HBIcloud is an online platform that integrates multi-omics tools to simplify complex data analysis for researchers without requiring extensive programming skills.

PICASO integrates single-cell gene expression data with biomedical databases to create networks that compare disease states and identify druggable targets.

This paper generates and evaluates computationally derived hallmarks of aging and cancer using DNA methylation data, showing stronger disease associations at the hallmark level than individual genes.

funkea provides a scalable Python tool for functional enrichment analysis in genetics using Spark, supporting pathway, cell-type, and tissue enrichment across annotation datasets.

The paper introduces a graph neural network framework that integrates multi-omics data with biological priors to identify biomarkers in Alzheimer's disease.

GenPPi 1.5 improves protein interaction prediction by integrating Random Forest with Reduced Interaction Sampling, enhancing performance in low sequence identity scenarios.

SocialGene creates and leverages knowledge graphs for multi-omics data analysis in genomics and drug discovery, enabling integration and exploration of diverse datasets.

This study identifies key genes, miRNAs, and pathways in cardiac arrest using NGS data and bioinformatics analysis.

The paper introduces a machine learning model that predicts metabolite associations with KEGG Level 3 pathways, achieving high accuracy and transfer learning to Level 2 categories.

The paper reconstructs patient-specific gene co-expression networks in lung adenocarcinoma to identify subtypes and survival predictive biomarkers.

This study introduces mosGraphFlow, a graph AI model for analyzing multi-omic data to identify AD-associated biomarkers and signaling pathways, achieving high classification accuracy.

mosGraphGPT is a foundation model for multi-omic signaling graphs trained on cancer data and fine-tuned for Alzheimer's Disease, improving classification accuracy and interpretability.

BEAM integrates multiple omics profiles with multiple clinical outcomes using bootstrap resampling to identify significant associations, outperforming existing methods in simulations and a pediatric leukemia application.