Bioinformatics and Genomic Networks

TurbOmics: a web-based platform for the analysis of metabolomics data using a multi-omics integrative approach

TurbOmics is a web-based platform that enables multi-omics integration of metabolomics, proteomics, and transcriptomics data, addressing limitations in metabolomics annotation and accessibility.

geneslator: an R package for comprehensive gene identifier conversion and annotation

geneslator provides a unified R package for gene identifier conversion, ortholog mapping, and pathway annotation across eight species, improving data integration and cross-species analysis.

Knowledge-Driven Interpretable Neural Networks for Mechanistic Insight

The paper introduces a knowledge-driven framework that models biological interactions in omics data using pathway graphs, enabling interpretable mechanistic insights into disease progression.

BLOBFISH: Bipartite Limited Subnetworks from Multiple Observations using Breadth-First Search with Constrained Hops

BLOBFISH identifies gene regulatory subnetworks connecting predefined gene sets using breadth-first search on bipartite graphs, applied to GTEx data to explore tissue-specific relationships.

Finetuning Foundation Models for Temporal Clinical Transcriptomics Data

The paper finetunes gene embeddings from foundation models for temporal clinical transcriptomics data, improving gene network analysis and detection of disease progression and drug response mechanisms.

Integrated metabolomics data analysis to generate mechanistic hypotheses with MetaProViz

The paper introduces MetaProViz, an R package for metabolomics data analysis that integrates prior knowledge to generate mechanistic hypotheses, using kidney cancer data to demonstrate its capabilities.

CompBio and MIRaS : A Multi-omic Analysis Platform Built on a Memory-Based Intelligence Engine

The paper introduces CompBio, a multi-omic analysis platform using MIRaS, a memory-based intelligence engine, to analyze omics data without relying on human-curated knowledgebases, producing traceable and statistically significant results.

LagCI Enables Inference of Temporal Causal Relationships from Dense Multi-Omic Time Series

LagCI identifies temporal causal relationships in dense multi-omics time series by analyzing lag-correlation profiles and filtering spurious associations.

Mango: Unearthing Patterns in Large-Scale Biological Data Through Interactive Correlation Analysis

Mango enables interactive correlation analysis of tabular biological data to distinguish biases from causal relationships in genomic and transcriptomic datasets.

GeneInsight: Condensing Gene Set Knowledge via Language Models

GeneInsight synthesizes gene set annotations using topic modeling and language models to provide interpretable summaries that reveal biologically significant insights overlooked by traditional enrichment analyses.

A Novel ILP Framework to Identify Compensatory Pathways in Genetic Interaction Networks with GIDEON

The paper introduces GIDEON, an ILP framework to identify compensatory pathways in yeast genetic interaction networks, finding new BPM gene sets with potential antifungal drug target insights.

DIVAS: an R package for identifying shared and individual variations of multiomics data

DIVAS identifies shared and individual variations in multiomics data, revealing partially shared patterns missed by existing methods.

hypeR-GEM: connecting metabolite signatures to enzyme-coding genes via genome-scale metabolic models

hypeR-GEM connects metabolite signatures to enzyme-coding genes using genome-scale metabolic models, showing concordance with proteomic and transcriptomic data in real-world applications.

A module-based approach for post-omics, post-GWAS network-based gene classification

The paper introduces ModGenePlexus, a network-based gene classification method that improves disease gene discovery by decomposing gene lists into modules and combining module-specific classifiers.

Complex Assembly and Activity States as Multifaceted Protein Attributes Explaining Phenotypic Variability

The paper introduces a multi-omics framework to analyze protein activity in context-specific networks, demonstrating its utility in linking molecular profiles to phenotypic outcomes using HeLa and Kyoto cell lines.

The landscape of plasma proteomic links to human organ imaging

The paper maps phenotypic and genetic links between plasma proteins and organ imaging traits using UK Biobank data, identifying associations and causal links across multiple organs.

OMIDIENT: Multiomics Integration for Cancer by Dirichlet Auto-Encoder Networks

OMIDIENT is a multi-omics integration method that uses Dirichlet auto-encoders to improve clustering, classification, and data reconstruction across cancer types.

PlantMDCS: A code-free, modular toolkit for rapid deployment of plant multi-omics databases

PlantMDCS provides a code-free, modular platform for deploying plant multi-omics databases, enabling efficient data management and analysis without programming expertise.

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.

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.

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.

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.

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.

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.

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.