Bioinformatics and Genomic Networks

The paper applies community detection to kinase networks derived from phosphoproteomics data to identify new signaling pathway components, finding TTK as a potential member of PI3K/AKT/mTOR signaling.

ioSearch integrates multi-omics data to identify disease-associated biomarkers by exploring inter-omics relationships and reducing multiple testing burdens.

This paper presents a large-scale RNA-Seq gene expression atlas of Lohmann White chickens, providing a resource for avian and mammalian research.

The paper introduces a web tool to rank and test human genes for expression abundance and invariance using GTEx data, with interactive parameter control and biological relevance validation.

This paper introduces a method to reconstruct signaling pathways by growing optimal DAGs from protein interaction networks and shows it outperforms existing methods.

MOBILE identifies context-specific molecular networks by integrating multi-omics data, revealing new regulatory mechanisms in immune and developmental pathways.

The paper compares machine learning methods for re-annotating bacterial genes to improve KEGG pathway network accuracy.

The paper introduces a phylogenetic profiling method using graph neural networks to reconstruct protein interactions across time, showing improved performance over traditional methods.

The paper predicts host-pathogen interactions for agricultural pathogens using an interolog-based approach and provides these interactions in a database for network analysis.

The paper models the plasma proteome of COVID-19 patients to identify proteins associated with disease severity using a graph convolutional network.

The paper uses UK Biobank data to predict protease networks linked to disease by analyzing genetic associations and phenome-wide results.

The paper develops an 'Unknome database' to prioritize understudied proteins and validates their functions using Drosophila RNAi and CRISPR/Cas9.

Prots2Net predicts protein-protein interactions using sequence data and STRING database interactions, outperforming methods that use sequence alone.

The paper uses network modeling to analyze how TP53 mutations affect gene regulation across cancer types and samples.

The paper identifies cancer-keeping genes (CKGs) as therapeutic targets by extending network structural controllability theory to bladder cancer and validates their cancer-suppressing effects through experiments.

The paper develops a framework to predict signaling pathway activity changes by integrating genetic perturbation data across cell lines, improving prediction accuracy using multi-task learning and supervised methods.

The paper identifies 18 essential genes for SARS-CoV-2 pathogenesis using unsupervised learning on biological networks and features.

The paper introduces MoFNet, a deep neural network that integrates multi-omic data to identify functional subnetworks associated with Alzheimer's Disease, showing replication in an independent cohort.

PathGPS uses GWAS summary data to identify genetic architecture by separating genetic components from environmental factors and finding gene-trait clusters associated with biological pathways.

BioNE integrates network embeddings for supervised learning tasks, enabling comparison of different embedding methods in biological contexts.

The paper develops NetKSA, a network-based machine learning framework to predict kinase-substrate associations, outperforming existing methods and enabling annotation of phosphosites targeted by less-studied kinases.

This study relaxes GWAS significance thresholds and uses GRIN to identify additional genes linked to suicidal behavior, which replicate across cohorts and show stronger biological connections.

NETSCOPE infers and analyzes biological networks using information-theoretic methods, validated on synthetic and real data across multiple domains.

The paper models COVID-19 disease biology to identify drug treatment candidates by analyzing protein-protein interactions and disease-associated genes.

MetaRelSubNetVis enables group-wise network visualizations with patient data and facilitates reliable sharing of visualizations between collaborators.

The paper introduces R-based tools for reproducible integration, exploration, and visualization of biological networks, with available code and documentation.

ProFeatMap provides a customizable 2D visualization tool for protein features, enabling users to explore domain and modification information from a list of proteins.

The paper identifies drug targets by analyzing breakdown points in protein-protein interaction networks of cancer fusions, showing improved patient survival in specific cancer subtypes.

TPAC improves cancer prognosis and tumor stage prediction by analyzing pathway dysregulation in tumor transcriptomics data using normal tissue-specific gene information.

UniFuncNet integrates data from multiple biological databases to create a flexible and comprehensive network for omics data analysis.