The Center for Computational Biology develops new and innovative methods of examining data in the biological sciences whose scale and complexity have historically resisted analysis.
CCB’s mission is to develop modeling tools and theory for understanding biological processes and to create computational frameworks that will enable the analysis of the large, complex data sets being generated by new experimental technologies.
- Meeting 10:00 - 11:00 a.m.
- Meeting 11:30 a.m. - 1:30 p.m.
For the past half-century, structural biologists relied on the notion that similar protein sequences give rise to similar structures and…bioRxiv
Gastrulation movements in all animal embryos start with regulated deformations of patterned epithelial sheets, which are driven by cell divisions,…Current Biology
We introduce a closure model for coarse-grained kinetic theories of polar active fluids. Based on a thermodynamically consistent, quasi-equilibrium approximation…arXiv
This is the simulation tool for tracking assemblies of microtubules driven by motor proteins.
This is a numerical computation package for various single- and double-layer kernels for Laplace and Stokes operators in boundary integral methods, implemented on top of the highly-optimized kernel independent fast-multipole method package PVFMM.
Data-driven predictions of gene expression, function, regulation, and interactions in human.
Deep learning-based algorithmic framework for predicting chromatin effects
Functional Networks of Tissues in Mouse
Genome-wide Scale functional interaction networks for 144 human tissues and cell types
Integrative Multi-species Prediction
K-Nearest Neighbors Imputation
This server performs in silico nano-dissection, an approach we developed to identify genes with novel cell-lineage specific expression.
Search-Based Exploration of Expression Compendium [Human]
Sleipnir Library for Computational Functional Genomics
A data-driven perspective to your gene expression profile for human tissues and diseases.