Bioinformatics and Genomic Networks

Inferring and Evaluating Network Medicine-Based Disease Modules with Nextflow

This paper introduces a Nextflow pipeline to evaluate disease module detection methods, showing variability in results due to method, network, and seed differences.

PIMO: Pathway-based Interpretable Multi-Omics interactions for multi-omics integration

PIMO improves multi-omics integration by modeling pathway-based interactions, enhancing survival analysis and interpretability in cancer datasets.

conMItion: an R package adjusting confounding factors for associations in multi-omics

conMItion adjusts confounding factors in multi-omics data to improve association measurements, identifying cancer-related genes and cell type associations in bladder and lung cancer.

Confounding effects of inferring gene co-expression networks from pooled data from different biological populations

This paper investigates how pooling data from different biological populations affects gene co-expression network analysis and finds that it disrupts module structure and is undetectable by common metrics.

Context-dependent correlations mislead transcriptomic network inference in bulk and single-cell data

The paper shows that pooled correlations in transcriptomic data often reverse sign when analyzed within specific contexts, highlighting the need for context-aware reporting of correlations.

MondrianMap: Navigating Gene Set Hierarchies with Multi-Resolution Enrichment Maps

MondrianMap provides multi-resolution visualization of gene set enrichment results, revealing directional oppositions and temporal dynamics across biological processes in cancer, aging, and exercise data.

Identifying gene regulation modules associated with tumor metastasis using a network decomposition approach and combinatorial fusion analysis

The paper develops a network decomposition approach with combinatorial fusion analysis to identify metastasis-associated gene regulatory modules, showing improved performance over existing methods.

SEMFA: A General Framework for Inferring Statistical Significance of Mahalanobis Similarity between Multi-Omics Profiled Samples Built on Multiple Factor Analysis

SEMFA infers the statistical significance of Mahalanobis similarity between multi-omics samples using multiple factor analysis, showing effectiveness in both Gaussian and non-Gaussian scenarios.

EnrichViz: An Interactive R Shiny Application for Visualization of Pathway Enrichment Results from Omics Data

EnrichViz provides an interactive R Shiny tool for visualizing pathway enrichment results from omics data without requiring coding.

MIND: Multimodal Integration with Neighbourhood-aware Distributions

The paper introduces a multi-omics integration framework that learns patient-specific embeddings from incomplete data using a variational autoencoder with a data-driven prior, achieving better performance on downstream tasks.

NERINE reveals rare variant associations in gene networks across phenotypes and implicates an SNCA-PRL-LRRK2 subnetwork in Parkinson's disease

NERINE tests gene network hypotheses with rare variant associations, identifying a subnetwork linked to Parkinson's disease.

Multivariate Random Forests for Cross-Modal Multi-Omics Integration

This paper introduces a random forest-based method for multi-omics integration that separates shared and modality-specific signals, showing improved clustering and biological insights in simulations and real datasets.

Mechanistic reconstruction of receptor-to-transcription factor signaling integrating prior knowledge and omics

The paper introduces SIGMA, a framework that reconstructs receptor-to-transcription factor signaling by integrating prior knowledge and omics data to uncover metabolic reprogramming mechanisms.

Drug-Prot: A query system for statistical inference of drug effects and interactions in dynamic proteomic networks

Drug-Prot quantifies drug effects and interactions using proteomic data, revealing protein-level causal relationships and actionable drug combinations.

Killiverse: an interactive multi-omics web resource for killifish

Killiverse provides an interactive web platform for exploring killifish multi-omics data, enabling cross-study and cross-species analysis through standardized datasets and ortholog mapping.

Evaluating microbial network inference methods: Moving beyond synthetic data with reproducibility-driven benchmarks

This paper evaluates six microbial network inference methods using real and generated data, showing that synthetic benchmarks can be misleading and proposing a reproducibility-focused framework for better evaluation.

OmniPath Metabo: chemical structures, interactions and mechanisms to study the metabolome

OmniPath Metabo integrates metabolomics data with prior knowledge to enable mechanistic analysis of the metabolome through harmonized databases and network construction.

Linear-time prediction of proteome-scale microbial protein interactions

FlashPPI predicts proteome-scale microbial protein interactions in linear time using contrastive learning and genomic language models, outperforming existing methods and enabling rapid network analysis.

Uncertainty-Aware Gene Rankings Reveal Key Players in Coexpression Networks

The paper introduces uncertainty-aware gene ranking methods to improve stability in coexpression network analysis, showing better recovery of reference and tissue-specific genes compared to traditional rankings.

IAN: An Intelligent System for Omics Data Analysis and Discovery

IAN is an R package that integrates and interprets omics data using a multi-agent AI system with LLMs, demonstrating its ability to avoid hallucination and support biological discovery.

GSPlot: Visualizing gene set similarity in enrichment results

GSPlot visualizes gene set similarity in enrichment results by displaying gene sets in a 2D space based on their enriched content similarity.

COMPASS enables cohort-independent digital biomarker discovery and pathway quantification

The paper introduces COMPASS, a method to quantify pathway activity from gene expression without reference cohorts, showing improved consistency and robustness compared to existing methods.

pFLEX – a Python library for fast functional evaluation of genetic networks at the biological module-level

pFLEX evaluates genetic networks using biological module-resolved precision recall metrics, improving runtime and offering new evaluation tools for network benchmarking.

Protein Language Models are Accidental Taxonomists

The paper shows that protein language models can exploit taxonomic information rather than true interaction features in PPI datasets, leading to inflated performance.

JASMINE: A powerful representation learning method for enhanced analysis of incomplete multi-omics data

JASMINE improves analysis of incomplete multi-omics data by learning representations that preserve modality-specific and shared information, achieving better performance across tasks.

go3: A Fast and Lightweight Library for Semantic Similarity of GO Terms and Genes

go3 provides a fast and lightweight Rust-based library for calculating semantic similarity of GO terms and genes, achieving significant speed and memory improvements over existing tools.

PAG-Agent: a biologist-oriented research assistant for context-aware pathway-level analysis and interpretation

PAG-Agent performs pathway-level analysis and interpretation of transcriptomic data, identifying biologically relevant pathways and generating hypotheses across datasets and conditions.

Ontologizer 3: a cross-platform desktop application for frequentist and Bayesian GO enrichment analysis

Ontologizer 3 provides a desktop tool for GO enrichment analysis using frequentist and Bayesian methods, showing higher precision with the Bayesian approach on simulated data.

inGSEA: An Improved Method for Gene Set Enrichment Analysis Using a Weighted Integral Statistic

inGSEA improves gene set enrichment analysis by using a weighted integral statistic, enhancing detection power and reducing computational burden.

Recon8D: A metabolic regulome network from oct-omics and machine learning

The paper uses machine learning to predict metabolomic variation across cancer cell lines using 8 omic layers, identifying key predictors and signaling pathways.