The goal of CCB Genomics research is to interpret and distill this complexity through accurate analysis and modeling of molecular pathways, particularly those in which malfunctions lead to the manifestation of disease. We are inventing integrative methods for systems-level pathway modeling through integrative analysis of genome-scale datasets.
Resolving chromatin remodeling-linked gene expression changes at cell type resolution is important for understanding disease states. We describe MAGICAL, a…
medRxivBK virus (BKV; human polyomavirus 1) infections are asymptomatic in most individuals, and the virus persists throughout life without harm.…
Journal of VirologyDNA methylation comprises a cumulative record of lifetime exposures superimposed on genetically determined markers. Little is known about methylation dynamics…
Molecular Systems Biology
Genome-wide Scale functional interaction networks for 144 human tissues and cell types
Deep learning-based algorithmic framework for predicting chromatin effects
This server performs in silico nano-dissection, an approach we developed to identify genes with novel cell-lineage specific expression.
Integrative Multi-species Prediction
Search-Based Exploration of Expression Compendium [Human]
K-Nearest Neighbors Imputation
Functional Networks of Tissues in Mouse
A data-driven perspective to your gene expression profile for human tissues and diseases.
Data-driven predictions of gene expression, function, regulation, and interactions in human.
Sleipnir Library for Computational Functional Genomics
