Bioinformatics and Genomic Networks

The paper describes a standardized approach to represent Inherited Metabolic Disorders in WikiPathways, highlights limitations in machine-readable disease information, and introduces methods for data curation and analysis using SPARQL queries.

The paper introduces a Python package for constructing context-specific biological networks using omics data and demonstrates its application in yeast fermentation.

Pathformer integrates multi-omics data using a pathway-informed Transformer to improve cancer diagnosis and prognosis prediction.

The paper introduces mantra, a method to estimate metabolic reaction activity changes from untargeted metabolomics data, demonstrating its utility in identifying biomarkers and enabling multi-omics integration.

This paper introduces a multi-network gene prioritization method for Alzheimer's Disease that complements mQTL-based approaches, showing significant concordance in prioritizing risk genes.

This study introduces a graph-based method to infer temporal signaling networks from high-resolution phosphoproteomics data, improving the identification of signaling-relevant interactions.

The paper introduces PIONEER, a deep learning framework for predicting protein-protein interfaces, and shows disease mutations are enriched in these interfaces, identifying oncoPPIs linked to cancer survival and drug responses.

This paper introduces a framework to integrate omics data types and generate interpretable embeddings by addressing regularization challenges in deep generative models.

This paper accelerates AlphaFold2-based PPI network prediction by 40x and reduces disk space by 4000x for 1000 proteins using a new protocol.

piTracer identifies synergistic drug targets by analyzing metabolomics data to reconstruct molecular cascades and predict combination therapies.

MOGAT improves multi-omics integration for cancer subtype prediction by addressing limitations in existing graph neural network approaches.

The paper introduces the hidecan R package for visualizing GWAS and differential expression results along with manually curated candidate genes.

aPEAR provides an R package for automated visualization of pathway enrichment networks by clustering similar pathways into meaningful biological themes.

This paper introduces ALaSCA, a causal analysis platform, and applies it to study antioxidant protein effects on Type 1 Diabetes progression using proteomic data.

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.

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

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

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 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.

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

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.

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

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.

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

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.

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

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.

The paper compares twelve methods for inferring gene co-expression networks and identifying functional communities in yeast, finding that CLR followed by Generalized Modularity Density clustering performs best.