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The CRIT framework for identifying cross patterns in systems biology and application to chemogenomics

Tara A Gianoulis12, Ashish Agarwal34, Michael Snyder5 and Mark B Gerstein346*

Author Affiliations

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

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Genome Biology 2011, 12:R32  doi:10.1186/gb-2011-12-3-r32

Published: 31 March 2011


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.