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.
A key unmet challenge in interpreting omics experiments is inferring biological meaning in the context of public functional genomics data.…
Nature MethodsEffective discovery of causal disease genes must overcome the statistical challenges of quantitative genetics studies and the practical limitations of…
Nature BiotechnologyA key challenge in precision medicine lies in understanding molecular-level underpinnings of complex human disease. Biological networks in multicellular organisms…
Journal of Molecular 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
