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.
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Center for Computational Biology
NetQuilt: A Powerful Computational Approach for Predicting Protein Function
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By stitching together an unprecedented diversity of data, a new software platform has enabled scientists to robustly predict what proteins do.
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.
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Upcoming Events
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26 Thu -
Meeting 11:00 a.m. - 12:00 p.m.
Genomics Group Meeting
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Meeting 11:00 a.m. - 12:00 p.m.
Publication Highlights
Sequence-structure-function relationships in the microbial protein universe
For the past half-century, structural biologists relied on the notion that similar protein sequences give rise to similar structures and…
bioRxivDeconstructing gastrulation at single-cell resolution
T. Stern, S. Shvartsman, E. F. Wieschaus
Gastrulation movements in all animal embryos start with regulated deformations of patterned epithelial sheets, which are driven by cell divisions,…
Current BiologyThermodynamically consistent coarse-graining of polar active fluids
Scott Weady, D. Stein, M. Shelley
We introduce a closure model for coarse-grained kinetic theories of polar active fluids. Based on a thermodynamically consistent, quasi-equilibrium approximation…
arXivDirector
Software
AMSOS (Active Matter Self-Organization Simulator)
This is the simulation tool for tracking assemblies of microtubules driven by motor proteins.
STKFMM
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.
humanbase
Data-driven predictions of gene expression, function, regulation, and interactions in human.
DeepSEA
Deep learning-based algorithmic framework for predicting chromatin effects
FNTM
Functional Networks of Tissues in Mouse
GIANT
Genome-wide Scale functional interaction networks for 144 human tissues and cell types
IMP 2.0
Integrative Multi-species Prediction
KNNimpute
K-Nearest Neighbors Imputation
Nano-Dissection
This server performs in silico nano-dissection, an approach we developed to identify genes with novel cell-lineage specific expression.
SEEK
Search-Based Exploration of Expression Compendium [Human]
Sleipnir
Sleipnir Library for Computational Functional Genomics
URSA(HD)
A data-driven perspective to your gene expression profile for human tissues and diseases.