Bioinformatics and Genomic Networks

eOmics: an R package for improved omics data analysis

eOmics is an R package that addresses limitations in omics data analysis by integrating ensemble methods, mediation models, and novel enrichment techniques, validated on single and multi-omics datasets.

Genome-scale enzymatic reaction prediction by variational graph autoencoders

The paper introduces MPI-VGAE, a framework for predicting metabolite-protein interactions across organisms, showing improved performance in reconstructing metabolic pathways and disease-specific networks.

Crosstalkr: An open-source R package to facilitatedrug target identification.

Crosstalkr identifies drug targets and disease subnetworks using protein-protein interaction networks and random walk methods.

NDEx IQuery: a multi-method network gene set analysis leveraging the Network Data Exchange

NDEx IQuery provides a tool for network-based gene set analysis using diverse pathways and integrates with existing resources to enhance pathway interpretation.

Network expansion of genetic associations defines a pleiotropy map of human cell biology

The paper uses network-based expansion of GWAS results to identify pleiotropic gene modules linked to multiple traits and demonstrates their application in studying inflammatory bowel disease.

Identification of novel prognostic targets in coronary artery disease and related complications using bioinformatics and next generation sequencing data analysis

The paper identifies novel prognostic targets in coronary artery disease using bioinformatics analysis of NGS data to find differentially expressed genes and hub genes.

Utilizing Pre-trained Network Medicine Models for Generating Biomarkers, Targets, Re-purposing Drugs, and Personalized Therapeutic Regimes: COVID-19 Applications

The paper introduces a pre-trained network medicine model, SEMO, to predict COVID-19 severity using DNA methylation data and identifies potential nutraceuticals and biomarkers.

PANGEA: A New Gene Set Enrichment Tool for Drosophila and Common Research Organisms

PANGEA is a new GSEA tool that allows flexible analysis of gene sets using various annotations, enhancing visualization and comparison of results for Drosophila and other organisms.

Network-based elucidation of colon cancer drug resistance by phosphoproteomic time-series analysis

The paper uses phosphoproteomic time-series data and the VESPA algorithm to analyze drug resistance mechanisms in colon cancer cells, revealing adaptive signaling rewiring confirmed by CRISPRko assays.

GENIUS: GEnome traNsformatIon and spatial representation of mUltiomicS data

GENIUS transforms multi-omics data into images for deep learning analysis to predict metastatic cancer progression from primary tumors.

Prioritizing Complex Disease Genes from Heterogeneous Public Databases

The paper introduces GRIPP, a framework to prioritize genes from multiple data modalities in GBM, showing iterative expansion and ranking can improve gene lists but may reduce modality-specific significance.

Inferring cell cycle phases from a partially temporal network of protein interactions

The paper infers cell cycle phases from a partially temporal network of protein interactions in budding yeast, showing that partial temporal data can reveal temporal organization.

PyGenePlexus: A Python package for gene discovery using network-based machine learning

PyGenePlexus predicts gene associations with input gene sets using network-based machine learning and provides interpretability through comparison with known gene sets.

An Expert-guided Hierarchical Graph Attention Network for Post-traumatic Stress Disorder Highlyassociative Genetic Biomarkers Identification

This paper develops a hierarchical graph attention network to identify PTSD-associated genetic biomarkers by integrating domain knowledge and deep learning.

Adaptation of Graph Convolutional Neural Networks and Graph Layer-wise Relevance Propagation to the Spektral library with application to gene expression data of Colorectal Cancer patients

The paper adapts graph convolutional neural networks and graph layer-wise relevance propagation to the Spektral library for analyzing gene expression data from colorectal cancer patients, identifying relevant genes and subnetworks.

Multi-omics data integration approach identifies potential biomarkers for Prostate cancer

This study integrates multi-omics data to identify potential biomarkers for prostate cancer by analyzing genomic, transcriptomic, and metabolomic datasets from diverse populations.

Comparison of gene set scoring methods for reproducible evaluation of multiple tuberculosis gene signatures

This study compares gene set scoring methods with original models for TB gene signatures, finding that gene set scoring can achieve equivalent or better performance than original models.

Prioritization of new candidate genes for rare genetic diseases by a disease-aware evaluation of heterogeneous molecular networks

The paper develops GLOWgenes, a disease-aware network-based algorithm to prioritize new candidate genes for rare genetic diseases by evaluating heterogeneous molecular networks.

netANOVA: novel graph clustering technique with significance assessment via hierarchical ANOVA

netANOVA identifies groups of graphs through hierarchical ANOVA-based clustering and significance testing, showing high performance in simulations and real data.

Open Biomedical Network Benchmark: A Python Toolkit for Benchmarking Datasets with Biomedical Networks

The paper introduces OBNB, a benchmarking resource for GNN-based network biology tasks, providing datasets and a Python toolkit for evaluating gene classification models.

Differential Expression Enrichment Tool (DEET): an interactive atlas of human differential gene expression

The paper develops DEET, a tool that reanalyzes differential gene expression data from multiple studies to help users compare their gene lists with existing results.

RNA-seq preprocessing and sample size considerations for gene network inference

This study evaluates preprocessing methods and sample size requirements for gene network inference using RNA-seq data, finding that no single method is optimal and recommending dataset-specific evaluations.

GOThresher: a program to remove annotation biases from protein function annotation datasets

GOThresher removes annotation biases from protein function annotation datasets to improve the quality of data for machine learning models.

cProSite: A web based interactive platform for online proteomics, phosphoproteomics, and genomics data analysis

cProSite provides an interactive platform for analyzing proteomics, phosphoproteomics, and genomics data from CPTAC to compare tumor and normal tissue molecular alterations.

Latent disease similarities and therapeutic repurposing possibilities uncovered by multi-modal generative topic modeling of human diseases

This paper uses multi-modal generative topic modeling to identify latent disease similarities and therapeutic targets for repurposing across human diseases.

BioNAR: An Integrated Biological Network Analysis Package in Bioconductor

BioNAR is a Bioconductor package for analyzing biological networks to quantify and rank vertices based on topology and clustering, enabling discovery of disease and function-enriched subcomplexes.

Accurately modeling biased random walks on weighted networks using node2vec+

The paper introduces node2vec+, an improved version of node2vec that incorporates edge weights in biased random walks, showing better performance on weighted gene networks.

Using graph-based model to identify cell specific synthetic lethal effects

This paper introduces a deep learning model using graph-based representation to predict cell-specific synthetic lethal gene pairs, enhancing cancer precision medicine.

Identification of Disease Modules Using Higher-Order Network Structure

The paper introduces a higher-order community detection method to identify disease modules in protein-protein interaction networks and GWAS data, revealing new disease-associated modules not found by conventional methods.

GENI: a web server to identify gene set enrichments in tumor samples

GENI is a web tool that identifies gene set enrichments in TCGA tumor data, simplifying analysis for non-experts by evaluating gene expression in the context of the whole transcriptome.