Method
The CRIT framework for identifying cross patterns in systems biology and application to chemogenomics
1 Department of Genetics, 77 Ave. of Louis Pasteur, Harvard Medical School, Boston, MA 02115, USA
2 Wyss Institute for Biologically - Inspired Engineering, 3 Blackfan Circle, Boston, MA 02115, USA
3 Department of Computer Science, Yale University, 51 Prospect St, New Haven, CT 06511, USA
4 Department of Molecular Biophysics and Biochemistry, Yale University, 266 Whitney Ave, New Haven, CT 06511, USA
5 Department of Genetics, Stanford University School of Medicine, Alway M344, Stanford, CA 94305, USA
6 Program in Computational Biology and Bioinformatics, Yale University, 266 Whitney Ave, New Haven, CT 06511, USA
Genome Biology 2011, 12:R32 doi:10.1186/gb-2011-12-3-r32
Published: 31 March 2011Abstract
Biological data is often tabular but finding statistically valid connections between entities in a sequence of tables can be problematic - for example, connecting particular entities in a drug property table to gene properties in a second table, using a third table associating genes with drugs. Here we present an approach (CRIT) to find connections such as these and show how it can be applied in a variety of genomic contexts including chemogenomics data.
