Bioinformatics and Genomic Networks

The paper demonstrates the versatility of MultiXrank, a Random Walk with Restart algorithm on multilayer networks, for gene prioritization, gene-disease association prediction, and disease signature computation.

vissE identifies and visualizes higher-order molecular phenotypes from functional enrichment analysis by summarizing redundancies into biological themes.

The paper presents a computational workflow integrating multi-omics and drug screening data from cell lines to identify druggable pathways in synucleinopathies.

The paper introduces GINtool, a software tool for regulon-based transcriptome analysis that accounts for regulatory activity modes and provides novel visualizations for functional category and operon analysis.

This paper introduces a graph convolutional network to predict disease-gene associations by leveraging external collaborative graphs and reducing noise through mutual information maximization.

The paper develops AutoTransOP, an autoencoder framework to translate omics signatures across species and contexts, outperforming existing methods in identifying predictive molecular features.

TEMINET improves diagnostic prediction by integrating multi-omics data through co-informative representations and trustworthy learning strategies, showing better performance across four diseases.

SignalingProfiler 2.0 integrates proteogenomic data with causal networks to generate mechanistic signaling models that recapitulate drug-perturbed phenotypes in breast cancer cells.

The paper benchmarks enrichment analysis methods using a disease pathway network and finds Network Enrichment Analysis methods perform best.

The paper evaluates deep learning models for protein-protein interaction prediction, finding they rely on data leakage and sequence similarity, and advocates for baseline comparisons.

PRRGO integrates differentially expressed genes from GEO with gene ontology data to generate PageRank-scored GO terms for hypothesis generation.

The paper aggregates RNA-seq data to infer consensus and tissue-specific gene co-expression networks, finding that larger sample sizes improve network quality and trait heritability annotations.

The paper introduces rROMA, a software package for computing gene set activity from omics data, and applies it to cystic fibrosis transcriptomic data to identify disease-related pathways and potential biases.

The paper curates 17 Drosophila signaling pathways using experimental evidence and integrates them into FlyBase with visualization tools.

NetVA identifies key molecules in disease-specific PPI networks using network vulnerability and EVC+ analysis, highlighting essential proteins and hubs in breast cancer.

The paper introduces IDSL.GOA, a tool for Gene Ontology analysis of metabolomic datasets, highlighting its ability to identify overrepresented GO terms not covered by existing pathway databases.

The paper investigates dark genes' role in genetic diseases and phenotypes using network and functional enrichment analyses across molecular datasets, identifying potential regulators and hub proteins associated with multiple diseases.

Genopyc is a Python library for analyzing complex diseases' genetic basis, enabling integration and visualization of genomic data to uncover dysregulated pathways.

GenePlexusZoo improves gene classification by integrating molecular networks from multiple species into a shared feature space, enhancing both single-species and cross-species predictions.

FAVA infers high-quality functional protein interaction networks from scRNA-seq and proteomics data, outperforming existing methods and identifying interactions for understudied proteins.

This study develops a multimodal fusion pipeline that improves survival prediction in cancer patients by outperforming unimodal models using The Cancer Genome Atlas data.

mosGraphGen generates multi-omics signaling graphs to enable integrative and interpretable graph AI model development by converting multi-omics data into biologically meaningful graphs.

The paper introduces secCellFie, a tool that infers secretory and metabolic pathway activity from omic data, demonstrating its application in immune cells, NAFLD hepatokine secretion, and antibody production in Chinese Hamster Ovary cells.

This paper introduces an axes-based network embedding method to uncover functional mechanisms of cells by analyzing the structure of embedding spaces rather than gene clusters.

collectNET infers cell-cell communication networks using three methods and provides an online tool with visualization and a human communication atlas.

This paper identifies potential pain-related genes using machine learning and interaction networks, finding JAK2/STAT3, ErbB, and Rap1 pathways as promising targets.

The paper annotates disease variants in the context of biological pathways to infer molecular phenotypes and model variant impacts.

The paper introduces a network permutation framework to assess how node degree influences edge prediction in biomedical networks and provides an edge prior as a baseline for performance evaluation.

iTraNet is a web tool for visualizing and analyzing trans-omics networks, revealing differences in hub molecule responses to glucose between wild-type and ob/ob mice.