Bioinformatics and Genomic Networks

Cluefish identifies gene clusters and biological functions in transcriptomic data series using over-representation analysis and prior knowledge, revealing retinoid signaling disruption as a sensitive pathway in zebrafish exposed to dibutyl phthalate.

The paper identifies key genes and pathways linking respiratory infections to heart failure exacerbation using bioinformatics and machine learning.

DeepGraphMut uses graph-based deep learning to predict cancer prognosis from somatic mutation data, identifying subtypes and achieving high survival prediction accuracy across 16 cancer types.

DriverOmicsNet integrates multi-omics data using GCNs and PPI networks to predict cancer-related features and identify driver genes across 15 cancer types.

This study integrates multi-omics data with gene regulatory networks to improve survival predictions in ten cancer types, showing improved associations with patient survival when GRNs are included.

Pathway Volcano simplifies differential expression visualization by filtering volcano plots to highlight pathway-related features, enabling better biological interpretation.

This study examines how disease module significance depends on module size and Gene-Disease Association scores, suggesting cutoffs for robust module construction.

This paper introduces a deep learning framework for integrating multimodal cancer data, showing improved performance and interpretability across TCGA datasets.

NetMedPy provides a computational package for efficient Network Medicine analyses, supporting diverse scoring functions and metrics for large-scale molecular screening.

DiCE identifies disease-associated genes by combining differential expression with network centrality analysis, validated on prostate cancer datasets to find genes correlated with disease-free survival.

The paper develops TWAVE, a generative model that identifies causal gene sets for complex traits by integrating transcriptomic data and gene perturbation responses.

The paper examines the relationship between transcriptomic stress responses and PPI network architecture in zebrafish embryos, finding stress response genes are more central in the network.

This study develops a machine learning model to predict kinase-substrate relationships from omics data, improving upon existing methods and providing a web tool for analysis.

This study integrates somatic mutation and RNA sequencing data using network-based stratification to identify cancer subtypes associated with survival and histology, revealing key driver genes.

The paper creates a functional landscape of human solute carriers by integrating multi-omics data and curated knowledge, assigning functions to each SLC and providing a web-accessible resource.

The paper predicts 196 new candidate proteins and identifies 14 sub-modules and hub proteins involved in rice seed development using an ensemble network-based approach.

The paper introduces a new pathway analysis method for longitudinal data, demonstrating its application in early type 1 diabetes studies with subtle biological signals.

The paper presents a manually curated gene list for teleost fish transcriptomics to improve pathway reconstruction accuracy in non-model organisms.

This study introduces DGP-AMIO, a graph attention network-based method that integrates multi-omics and gene interaction data to identify novel disease genes, achieving high performance across 20 disease datasets.

The paper identifies DNA damage-associated protein co-expression modules in cardiomyocytes and shows their relationship to genetic variation tolerance and CVD risk.

This paper introduces SGTCCA-Net, a framework for multi-omics network inference that addresses limitations of existing methods by incorporating higher-order correlations and improving computational efficiency.

This paper introduces a method to improve protein-protein interaction prediction by using topology-driven negative sampling and an ML pipeline that enhances generalizability and interpretability.

This paper introduces GGOT to detect critical transitions and identify trigger molecules in disease progression by analyzing data distributions and transport maps across disease stages.

GeneCOCOA identifies context-specific functions of individual genes using co-expression data and curated functional gene sets, outperforming existing methods in gene-disease association recall.

miss-SNF integrates multimodal patient data with missing sources by recovering patient similarities and identifying clinically relevant subgroups.

The paper consolidates RNA-seq and microarray data to infer gene-pathway associations in Tetrahymena thermophila, identifying known and novel functionally related genes through co-expression clustering.

The paper evaluates sequence and structural similarity metrics for predicting shared paralog functions using synthetic lethality and protein-protein interactions.

This study uses gene set embeddings to uncover biological function associations across species by integrating node2vec with MSigDB data.

This study uses a single-cell network approach to analyze metabolic regulation in gynecologic and breast cancers, identifying key transcriptional regulators and metabolic pathways involved in tumor heterogeneity.

The paper develops scNET to infer cell-type-specific co-expression networks from single-cell data, enhancing the human interactome for improved disease gene prediction.