Bioinformatics and Genomic Networks

GSEA and the coexpression network approach identify novel pathway connections of molecular processes affected in Porto-sinusoidal vascular disease.

The paper uses GSEA and WGCNA to identify dysregulated pathways in Porto-sinusoidal vascular disease, revealing immune, signaling, and metabolic dysfunctions.

Transcriptomic Profiling and Regulatory Network Analysis of Ten Metabolic Transporters Across Five Diabetic Complications: A Multi-Dataset, Twelve-Phase GEO Bioinformatics Study

This study identifies shared and complication-specific dysregulation of ten metabolic transporters across five diabetic complications using a multi-phase bioinformatics analysis.

There and back again: a multi-omics tale of thyroid co-expression network rewiring

The paper develops a framework for multi-omics WGCNA and differential network analysis to study thyroid toxicity, finding extensive molecular disruption and partial recovery.

A protein interactome for the last eukaryotic common ancestor illuminates the biochemical basis of modern genetic diseases

The paper reconstructs the protein interactome of the Last Eukaryotic Common Ancestor and identifies new human gene-disease associations for bone density and congenital birth defects.

ARACoFusion: Uncertainty-aware calibrated deep learning for protein-protein interaction network prediction in Arabidopsis thaliana

ARACoFusion predicts protein-protein interactions in Arabidopsis thaliana using a deep learning model that improves accuracy and reliability through uncertainty-aware techniques and benchmarking against existing predictors.

GAE-Δ: A Graph-Learning Framework for Gene Network Rewiring and Clinical Outcome Prediction from Multi-Omics Data

GAE-Δ uses a graph autoencoder to analyze multi-omics data and predict clinical outcomes while identifying gene network rewiring associated with cancer progression.

GenesetDiseaseDrugNetwork (GDDN): a web server for disease enrichment and drug prioritization

GDDN connects gene-sets to diseases and therapeutics to prioritize drugs, enabling functional analysis and personalized medicine.

Temporal Phosphoproteomic Network Reconstruction of Condition-Dependent EGFR Signaling Routes Through Unmeasured Proteins

The paper introduces TS-BFS-BS to reconstruct temporal phosphoproteomic networks by integrating time-course data with STRING priors, identifying signaling routes through unmeasured proteins.

mmContext: an open framework for multimodal contrastive learning of omics and text data

The paper introduces mmContext, a framework for multimodal contrastive learning of omics and text data, and demonstrates its utility in RNA-Seq and text integration tasks.

Shiny AMMOA: an interactive platform for integrative multi-omics analysis of murine aging

Shiny AMMOA provides an interactive platform for analyzing multi-omics datasets from murine aging studies, enabling integrative analysis and visualization of transcriptomic, proteomic, and metabolomic data.

Dual-channel graph learning reveals similarity and complementarity in protein-protein interaction networks

DMG-PPI is a dual-channel graph neural network that models similarity and complementarity in PPI networks, improving prediction accuracy and providing interpretable insights into binding mechanisms.

ProteomeLM: A proteome-scale language model enables accurate and rapid prediction of protein-protein interactions and gene essentiality across taxa

ProteomeLM predicts protein-protein interactions and gene essentiality across species using a proteome-scale language model, outperforming existing methods in speed and accuracy.

AWmeta empowers adaptively-weighted transcriptomic meta-analysis

AWmeta improves transcriptomic meta-analysis by adaptively weighting studies, yielding more accurate differentially expressed genes with fewer samples and reduced false positives.

TransXplorer: An automated translational discovery platform for RNA-seq data

TransXplorer provides a unified web platform for RNA-seq analysis, integrating data processing, batch correction, functional enrichment, and drug candidate linking across multiple datasets.

Knowledge Inclusive Machine Learning for Disease Gene Prioritisation

This paper introduces KIML, a machine learning framework that integrates experimental data with biological knowledge to improve disease gene prioritisation, showing better performance than existing methods.

MetaNet: a scalable and integrated tool for reproducible omics network analysis

MetaNet is an R package for scalable multi-omics network analysis, offering faster computation and better memory efficiency than existing tools, with applications in microbial and exposome-transcriptome studies.

MagNet: Computational Methods for Constructing High-Confidence Protein-Protein Interaction Networks in Magnaporthe oryzae

This study constructs a reliable PPI network for Magnaporthe oryzae using three computational methods, resulting in a database with 217,531 highly confident interactions.

Protein Stability, Turnover Kinetics, and Abundance Constrain the Scaling of Protein Interaction Networks

The paper identifies how protein stability, turnover, and abundance influence the connectivity of protein-protein interaction networks in yeast.

A Condition-Informed Framework for Evaluating Temperature-Responsive Gene Sets Using Protein Interaction Data

This paper develops a framework to evaluate temperature-responsive gene sets using protein interaction data, showing that restricting second-order connections to differentially expressed genes improves biological validation.

BRIDGE prioritizes human and mouse gene correspondences using functional and disease evidence

BRIDGE prioritizes human-mouse gene correspondences using functional and disease evidence to improve gene selection for disease modeling and drug screening.

Uncertainty-aware graph representation learning with positive-unlabeled classification for biomarker discovery in peripheral artery disease

This paper develops an uncertainty-aware framework for PAD biomarker discovery using graph neural networks and positive-unlabeled learning, identifying novel and validated candidate proteins.

PEARL: Integrative multi-omics classification and omics feature discovery via deep graph learning

PEARL is a deep graph learning method for multi-omics integration that outperforms existing methods and identifies functionally important genes in Alzheimer's disease.

Focus on the edges: a biomolecular network of histone PTMs, metabolites and proteins unveils functional entanglement in AML

The paper constructs a multi-omics biomolecular network in AML using proteins, metabolites, and histone PTMs to reveal functional entanglement between biomolecules.

Beyond Pathway Boundaries: A Degree-Aware Network Clustering Test for Gene Sets

The paper introduces MANGO, a network-based method to detect gene set clustering by conditioning on network topology and gene degree, showing improved performance over existing approaches.

ORBIT: Orthogonal Rotation for Biological Inter-species Transfer

ORBIT aligns cross-species coexpression network embeddings using orthogonal Procrustes rotation, achieving higher cross-species retrieval quality than autoencoders.

Stability and Performance of Linear Combination Tests of Gene Set Enrichment for Multiple Covariance Estimators in Unbalanced Studies

This paper evaluates the performance of linear combination tests for gene set enrichment using different covariance estimators in unbalanced studies, finding ridge estimation more reliable for unbalanced designs.

A network medicine framework for multi-modal data integration in therapeutic target discovery

This paper presents a network medicine framework integrating multi-modal data to identify therapeutic targets in clear cell renal cell carcinoma, recovering established targets and predicting five candidates, with ENO2 inhibition showing the strongest anti-tumour effect.

SNPWay: streamlined SNP-to-function and pathway over-representation analysis

SNPWay provides a streamlined workflow for SNP-to-function and pathway over-representation analysis using integrated tools and a web server.

The Limits of Cross-Species WGCNA: Library Imbalance and Signal Dilution Constrain Effector Gene Recovery in Dual-Organism RNA-seq

This study evaluates WGCNA's ability to recover effector genes in dual-organism RNA-seq by simulating varying library ratios and signal strengths.

Network-based analysis of glioblastoma identifies patient communities and cluster-specific biomarkers

This study uses network-based analysis of TCGA-GBM data to identify six glioblastoma tumor communities and their cluster-specific biomarkers.