Bioinformatics and Genomic Networks

PREDICTING GENE DISEASE ASSOCIATIONS WITH KNOWLEDGE GRAPH EMBEDDINGS FOR DISEASES WITH CURTAILED INFORMATION

The paper develops two novel knowledge graph embedding algorithms, DLemb and BioKG2Vec, which outperform existing methods in predicting gene-disease associations, particularly for diseases with limited information.

ODAMNet: a Python package to identify molecular relationships between chemicals and rare diseases using overlap, active module and random walk approaches

Transfer Learning Of Gene Expression Using Reactome

This paper introduces a gene expression transfer learning model that enables cross-species gene mapping using reactome profiles, improving functional gene understanding and drug response prediction.

Explorative Discovery of Gene Signatures and Clinotypes in Glioblastoma Cancer Through GeneTerrain Knowledge Map Representation

This paper introduces GTKM, a method for visualizing gene expression data in glioblastoma, showing it identifies gene patterns linked to clinical outcomes and subtypes.

G-S-M: A Comprehensive Framework for Integrative Feature Selection in Omics Data Analysis and Beyond

The paper introduces G-S-M, a framework for integrative feature selection that combines machine learning and prior knowledge to identify discriminative feature groups for medical applications.

Immediate-Early Genes as Influencers in Genetic Networks and their Role in Alzheimer's Disease

The paper investigates the role of immediate-early genes (IEGs) in genetic networks and identifies MARK4 as a potential therapeutic target for Alzheimer's disease using network analysis and GWAS data.

Exploring the Knowledge of An Outstanding Protein to Protein Interaction Transformer

This paper introduces PPITrans, a Transformer-based model for protein-protein interaction prediction that outperforms existing methods on unseen species.

PathIntegrate: Multivariate modelling approaches for pathway-based multi-omics data integration

PathIntegrate integrates multi-omics data using pathway-based modeling to identify important pathways and molecules contributing to outcomes, demonstrated on COPD and COVID-19 datasets.

Uncovering organizing principles of protein-protein interaction networks

This study examines protein-protein interaction networks, validating power laws, identifying network compression during evolution, and exploring hub connectivity patterns.

Drug Contraindications in Comorbid Diseases: a Protein Interactome Perspective

The paper identifies drug contraindications in comorbid diseases by analyzing drug target networks and their association with disease-specific protein interaction networks and pathways.

Multi-Scale Geometric Network Analysis Identifies Melanoma Immunotherapy Response Gene Modules

This study identifies gene modules associated with melanoma immunotherapy response using dynamic Ollivier-Ricci curvature analysis of gene expression data.

Charting cellular differentiation trajectories with Ricci flow

This paper applies Ricci flow to model network rewiring during cellular differentiation using single-cell RNA-seq data, showing that network entropy and Ricci curvature provide complementary insights.

Transkingdom Network Analysis (TkNA): a systems approach inferring causal factors underlying host-microbiota interactions.

The paper introduces TkNA, a causal-inference framework for analyzing host-microbiota multi-omics data to identify master regulators of interactions.

playOmics: A multi-omics pipeline for interpretable predictions and biomarker discovery

playOmics is an open-source R package for multi-omics analysis that enables biomarker discovery and interpretable predictions through logistic regression models and visualization tools.

NetActivity enhances transcriptional signals by combining gene expression into robust gene set activity scores through interpretable autoencoders

NetActivity improves gene set activity scores by using interpretable autoencoders, showing better consistency and biological relevance in prostate cancer analysis.

Multifaceted Representation of Genes via Deep Learning of Gene Expression Networks

The paper develops GeneRAIN models trained on bulk RNA-seq data to predict gene attributes, showing they capture biological information beyond gene expression and outperform existing models.

MOCAT: Multi-Omics Integration with Auxiliary Classifiers Enhanced Autoencoder

MOCAT improves multi-omics data integration using auxiliary classifiers and attention mechanisms, achieving better classification and biomarker discovery than existing methods.

GDOP: A graph convolutional network-based drug "on-target" pathway prediction algorithm

GDOP predicts drug 'on-target' pathways using graph convolutional networks, achieving high AUC-ROC and Top10 accuracy on LINCS data.

Generalized Reporter Score-based Enrichment Analysis for Omics Data

The paper introduces GRSA, a generalized reporter score-based method for multi-group and longitudinal omics data, showing increased sensitivity and applied it to microbiome, transcriptome, and metabolome data.

Comparing cellular response to two radiation treatments based on key features visualization

The paper visualizes key features of cellular responses to two radiation treatments using network-based tools and statistical analysis of transcriptomic data.

Modeling cancer dependency with deep graph models

The paper introduces a deep learning framework called DepGPS to model tumor vulnerabilities using graph neural networks and transformers, showing improved prediction of context-specific vulnerabilities and utility in identifying synthetic lethal genes.

Gene Expression Reactomes Across Species Do Not Correlate with Gene Structural Similarity

The paper measures gene expression responses to drugs in mouse and rat genomes across organs and finds that reactomes do not correlate with structural similarity, proposing a new cross-species mapping approach based on expression patterns.

A Model-agnostic Computational Method for Discovering Gene-Phenotype Relationships and Inferring Gene Networks via in silico Gene Perturbation

This paper introduces a model-agnostic method to discover gene-phenotype relationships and infer gene networks using in silico gene perturbation, showing improved prognostic performance over conventional biomarkers.

transomics2cytoscape: An automated software for interpretable 2.5-dimensional visualization of trans-omic networks

The paper introduces transomics2cytoscape, an R package for automated 2.5D visualization of trans-omics networks, enabling rapid and dynamic interpretation of multi-omics data.

Fast and scalable querying of eukaryotic linear motifs with gget elm

gget elm enables fast, scalable querying of Eukaryotic Linear Motifs from the ELM database using local computations.

CryptoCEN: A Co-Expression Network for Cryptococcus neoformans reveals novel proteins involved in DNA damage repair

CryptoCEN, a co-expression network for Cryptococcus neoformans, identifies 13 novel DNA damage repair genes with 100% predictive accuracy.

Evaluation of single-sample network inference methods for precision oncology

This study evaluates single-sample network inference methods for cancer subtyping using bulk RNA-seq data from cancer cell lines.

Random Walk with Restart on multilayer networks: from node prioritisation to supervised link prediction and beyond

The paper demonstrates the versatility of MultiXrank, a Random Walk with Restart algorithm on multilayer networks, for gene prioritization, gene-disease association prediction, and disease signature computation.

vissE: A versatile tool to identify and visualise higher-order molecular phenotypes from functional enrichment analysis

vissE identifies and visualizes higher-order molecular phenotypes from functional enrichment analysis by summarizing redundancies into biological themes.

Statistical integration of multi-omics and drug screening data from cell lines

The paper presents a computational workflow integrating multi-omics and drug screening data from cell lines to identify druggable pathways in synucleinopathies.