Bioinformatics and Genomic Networks

Gene interaction enrichment analysis for transcriptomic data with GREA

GREA improves gene set enrichment analysis by incorporating gene interactions, outperforming existing tools in detecting pathways related to respiratory viral infections.

A Generalized Higher-order Correlation Analysis Framework for Multi-Omics Network Inference

This paper introduces SGTCCA-Net, a framework for multi-omics network inference that addresses limitations of existing methods by incorporating higher-order correlations and improving computational efficiency.

Graph-Based Modeling of Alzheimer's Protein Interactions via Spiking Neural, Hyperdimensional Encoding, and Scalable Ray-Based Learning

This paper introduces a computational framework for predicting Alzheimer's protein interactions using graph-based learning, spiking neural networks, and hyperdimensional encoding.

Topology-Driven Negative Sampling Enhances Generalizability in Protein-Protein Interaction Prediction

This paper introduces a method to improve protein-protein interaction prediction by using topology-driven negative sampling and an ML pipeline that enhances generalizability and interpretability.

Uncovering critical transitions and molecule mechanisms in disease progressions using Gaussian graphical optimal transport

This paper introduces GGOT to detect critical transitions and identify trigger molecules in disease progression by analyzing data distributions and transport maps across disease stages.

RNAchat: Integrating machine learning algorithms to identify metapathways based on clinical and multi-omics data

RNAchat integrates machine learning with clinical and multi-omics data to identify metapathways and cellular interactions, improving understanding of disease mechanisms like RA and HF.

RNAcompare: Integrating machine learning algorithms to unveil the similarities of phenotypes based on clinical, multi-omics using Rheumatoid Arthritis and Heart Failure as Case Studies

RNAcompare integrates clinical and multi-omics data using machine learning to identify phenotypic similarities in rheumatoid arthritis and heart failure, revealing shared molecular pathways and age-related subgroups.

GeneCOCOA: Detecting context-specific functions of individual genes using co-expression data

GeneCOCOA identifies context-specific functions of individual genes using co-expression data and curated functional gene sets, outperforming existing methods in gene-disease association recall.

miss-SNF: a multimodal patient similarity network integration approach to handle completely missing data sources

miss-SNF integrates multimodal patient data with missing sources by recovering patient similarities and identifying clinically relevant subgroups.

Inferring gene-pathway associations from consolidated transcriptome datasets: an interactive gene network explorer for Tetrahymena thermophila

The paper consolidates RNA-seq and microarray data to infer gene-pathway associations in Tetrahymena thermophila, identifying known and novel functionally related genes through co-expression clustering.

Evaluating Sequence and Structural Similarity Metrics for Predicting Shared Paralog Functions

The paper evaluates sequence and structural similarity metrics for predicting shared paralog functions using synthetic lethality and protein-protein interactions.

Systematic Comparative Analysis of Pathway Databases from a User-Perspective

The paper systematically compares pathway databases by querying them with genes, pathways, and conditions, finding that PathDIP and Reactome perform best depending on the query type.

3D IntelliGenes: AI/ML application using multi-omics data for biomarker discovery and disease prediction with multi-dimensional visualization

This paper introduces 3D IntelliGenes, a tool for visualizing AI/ML results on multi-omics data to discover biomarkers and predict diseases through 3D clustering and feature analysis.

Uncovering Latent Biological Function Associations through Gene Set Embeddings

This study uses gene set embeddings to uncover biological function associations across species by integrating node2vec with MSigDB data.

Deep Integrated Network Analysis: a data-driven tool to discover and characterize disease pathways in the liver

The paper constructs a comprehensive liver-specific gene interaction network using transcriptomic data from multiple species to enable unbiased analysis of liver disease pathways.

A Single-Cell Network Approach to Decode Metabolic Regulation in Gynecologic and Breast Cancers

This study uses a single-cell network approach to analyze metabolic regulation in gynecologic and breast cancers, identifying key transcriptional regulators and metabolic pathways involved in tumor heterogeneity.

Augmenting the human interactome for disease prediction through gene networks inferred from human cell atlas

The paper develops scNET to infer cell-type-specific co-expression networks from single-cell data, enhancing the human interactome for improved disease gene prediction.

SEHI-PPI: An End-to-End Sampling-Enhanced Human-Influenza Protein-Protein Interaction Prediction Framework with Double-View Learning

SEHI-PPI predicts human-influenza protein-protein interactions using a double-view deep learning framework with adaptive negative sampling, outperforming existing methods and demonstrating strong generalizability.

EasyPubPlot: a shiny web application for rapid omics data exploration and visualization

EasyPubPlot is a user-friendly Shiny web app and R package that simplifies omics data visualization, generating publishable plots with minimal steps.

Pathway redistribution across cellular states reveals a shared signaling backbone and context-dependent regulatory modules in RNA-binding protein networks

The paper identifies pathway redistribution across cellular states using a framework combining co-expression data and deep learning, revealing shared signaling modules and context-dependent regulatory networks in RNA-binding proteins.

Unlocking hidden flaws in PPV and ACC: A step towards more reliable identification of protein complex

This paper identifies a flaw in PPV and ACC calculations for protein complex detection and proposes revised metrics, demonstrating improved reliability through benchmark experiments.

Predicting the Pathway Involvement of Compounds Annotated in the Reactome Knowledgebase

The paper predicts pathway involvement of unannotated compounds using Reactome data, achieving higher accuracy than KEGG-based models.

CoPPIs Algorithm: A Tool to Unravel Protein Cooperative Strategies in Pathophysiological Conditions

The paper introduces CoPPIs, an algorithm that identifies differentially correlated functional modules by combining PPIs and co-expression networks, applied to proteomic data from Parkinson's disease and GBA1 mutation cases.

MOSim: bulk and single-cell multi-layer regulatory network simulator

MOSim generates synthetic bulk and single-cell multi-omics datasets for benchmarking methods that analyze regulatory networks across various omics layers.

Higher-Order Biomarkers Through Network Motif Mining: A COVID-19 Case Study

The paper introduces a motif mining approach to identify higher-order biomarkers from expression data, showing improved performance in classification and clustering compared to traditional methods in a COVID-19 study.

Integrative systems biology framework discovers common gene regulatory signatures in multiple mechanistically distinct inflammatory skin diseases

The paper identifies common gene regulatory signatures across multiple inflammatory skin diseases using an integrative systems biology framework and proposes potential drug targets.

ORCO: Ollivier-Ricci Curvature-Omics: an unsupervised method for analyzing robustness in biological systems

ORCO is an unsupervised network-based tool that computes Ollivier-Ricci curvature to analyze biological network robustness and identify therapeutic targets.

Integrative Multi-Scale Network Simulation forPrecision Drug Repurposing with PETS

PETS simulates drug-gene interactions using multi-scale network propagation to identify and rank drug candidates for reversing disease-specific gene expression signatures.

Cutting through the clutter: minimizing redundancy in GO enrichment analysis with evoGO

evoGO minimizes redundancy in GO enrichment analysis by considering the GO hierarchy, improving specificity while preserving relevant biological information.

Knowledge Discovery in Datasets of Proteomics by Systems Modeling in Translational Research on Pancreatic Cancer

The paper proposes a systems modeling workflow for knowledge discovery in proteomics datasets, applied to pancreatic ductal adenocarcinoma to identify translational targets and pathways.