Bioinformatics and Genomic Networks

MultiGEOmics: Graph-Based Integration of Multi-Omics via Biological Information Flows

MultiGEOmics integrates multi-omics data using graph-based methods to model cross-omics regulatory signals and handle missing data, showing strong performance across cancer and Alzheimer's datasets.

EMTscore infers divergent EMT pathways from omics data and enables rapid screening for correlated gene sets

EMTscore provides an efficient tool for unbiased scoring of EMT pathways in single-cell or bulk omics data and enables rapid screening of gene set correlations.

PAGAN predicts digenic interactions by generalizing single-gene representations in biological networks

PAGAN predicts digenic interactions by learning network representations of gene pairs from single-gene data in biological networks.

Unifying the genetic landscape of common and rare diseases via latent neighborhoods

This paper develops a deep-learning method to unify genetic evidence from common and rare diseases by integrating diverse data sources and identifying shared genetic mechanisms.

A network analysis for the identification of gene modules in the transcriptome during Nicotiana benthamiana interfamily grafting

The paper uses Bayesian network analysis to identify gene modules in Nicotiana benthamiana during interfamily grafting, revealing six temporal expression patterns and functional enrichments.

X-intNMF: A Cross- and Intra-Omics Regularized NMF Framework for Multi-Omics Integration

X-intNMF improves multi-omics integration by modeling both cross-omics and intra-omics interactions, outperforming existing methods in cancer phenotype prediction and survival analysis.

BiOmics: A Foundational Agent for Grounded and Autonomous Multi-omics Interpretation

BiOmics introduces an AI agent that improves multi-omics interpretation by combining reasoning and embedding spaces, outperforming existing methods in variant discovery, cell annotation, and drug repurposing.

RadarOmics: Intuitive visualisation of multidimensional omics data in ecological, evolutionary, and developmental studies

RadarOmics provides a visualization tool for multidimensional omics data, revealing coordinated molecular responses across biological processes in ecological, evolutionary, and developmental studies.

iModMix: Integrative Module Analysis for Multi-omics Data

iModMix identifies novel multi-omics associations by constructing data-driven modules without relying on biological annotations.

KG2ML: Integrating Knowledge Graphs and Positive Unlabeled Learning for Identifying Disease-Associated Genes

KG2ML identifies disease-associated genes not in existing knowledge graphs using positive unlabeled learning and integrates with machine learning for improved classification.

Predicting the pathway involvement of metabolites annotated in the MetaCyc knowledgebase

The paper trains and evaluates machine learning models to predict pathway associations of metabolites using MetaCyc data, achieving performance comparable to KEGG-based models.

ChemGenXplore: An Interactive Tool for Exploring and Analysing Chemical Genomic Data

ChemGenXplore provides an interactive web tool for visualizing and analyzing chemical genomic data, enabling users to explore pre-implemented and uploaded datasets with various analytical features.

Network-based analysis of genome-wide biobank data boosts discovery of genetic associations in psoriasis

The paper applies network-based methods to genome-wide biobank data to discover genetic associations in psoriasis, identifying known genes and novel MRPL family genes.

User-driven development and evaluation of an agentic framework for analysis of large pathway diagrams

The paper describes the development and evaluation of an agentic framework using LLMs to explore molecular interaction maps through user-driven prototyping and testing.

SGCRNA: Spectral Clustering-Guided Co-Expression Network Analysis Without Scale-Free Constraints for Multi-Omic Data

SGCRNA improves upon WGCNA by removing scale-free constraints and parameter tuning, offering a more flexible co-expression network analysis for multi-omic data.

Multi-omics guided pathway and network analysis of clinical metabolomics and proteomics data

The paper provides a guide for analyzing metabolomics data in R, integrating it with proteomics using multi-omics factor analysis and network-based approaches to discover biomarkers and understand disease mechanisms.

OmniCorr: An R-package for visualizing putative host-microbiota interactions using multi-omics data

OmniCorr is an R package that clusters and visualizes multi-omics data to identify putative host-microbiota interactions across different biological systems.

Functional Annotation of Novel Heat Stress-responsive Genes in Rice Utilizing Public Transcriptomes and Structurome

The paper functionally annotates heat stress-responsive genes in rice by integrating transcriptome data with structural information from AlphaFold, identifying genes related to metal homeostasis.

Breast Cancer Subtyping with HyperCLSA: A Hypergraph Contrastive Learning Pipeline for Multi-Omics Data Integration

This paper introduces HyperCLSA, a deep learning framework for breast cancer subtyping using multi-omics data integration, achieving 90.1% accuracy on TCGA-BRCA for PAM50 classification.

Synteny: a high throughput web tool to streamline causal gene prioritisation and provide insight into protein function

Synteny prioritizes disease-relevant genes and identifies functionally important proteins using human genetic evidence.

Immune gene co-expression networks are constrained by a non-linear topology spectral relationship

The paper analyzes immune gene co-expression networks across two cohorts and finds that neither modularity nor spectral dominance alone distinguishes healthy from disease states, but their joint distribution reveals a geometric constraint.

HUBMet: An integrative database and analytical platform for human blood metabolites and metabolite-protein associations

HUBMet is a database and platform providing metabolite and metabolite-protein associations with analytical tools, demonstrating utility in identifying metabolic signatures linked to COVID-19 severity.

A Heterogeneous Graph Framework for Inference of Metabolite-Protein-Drug Interaction Networks

The paper introduces TopoMPI, a graph-based framework for predicting metabolite-protein-drug interactions by integrating multiple biological relationships and achieving AUCs between 0.79 and 0.86 across various datasets.

Understanding the LLM-based gene embeddings

This study evaluates LLM-derived gene embeddings for pathway information recovery using GSEA, showing they encode substantial biological knowledge.

Identifying High-Dimensional Genomic Factors Modulating Biological Networks Across Multi-Omics Data

GFBioNet identifies genomic factors modulating trait-trait relationships in multi-omics data, revealing host genetics, microbial taxa, and mutations affecting biological networks.

DAWN-SCAPE: Discovering Associations With Networks through Shared Contextual Analysis of Phenotype and Expression

DAWN-SCAPE distinguishes between genes causing phenotypic differences and those affected by the phenotype, applying it to autism and Alzheimer's data to identify upstream and downstream biological processes.

Unraveling the network signatures of oncogenicity in virus-human protein-protein interactions

The paper identifies viral oncogenic potential by analyzing protein-protein interaction networks to uncover mechanisms linking viral infections to cancer development.

SPARK: deciphering tumor-specific signaling networks through an integrative predictive model

SPARK predicts kinase-substrate associations across cancers using a multi-modal machine learning model, revealing tissue-specific phosphorylation networks and therapeutic targets.

Epigenetic control of metabolic identity across cell types

The paper reconstructs metabolic networks across 58 human tissues and cell types using RNA-seq and epigenomic data, identifying core and cell-type-specific metabolic functions and their regulatory elements.

Complementary human gene interaction maps from radiation hybrids and CRISPRi

This paper compares two human genetic interaction maps generated using radiation hybrids and CRISPRi, showing they share some interactions but differ in gene pair specificity and GWAS network overlap.