Inferring interactions, expression programs and regulatory networks from high throughput biological data

(cont.) For the networks level I present an algorithm that efficiently combines complementary large-scale expression and protein-DNA binding data to discover co-regulated modules of genes. This algorithm is extended so that it can infer sub-networks for specific systems in the cell. Finally, I present an algorithm which combines some of the above methods to automatically infer a dynamic sub-network for the cell cycle system.

Department dc:contributor.department

Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science.

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2003

Author dc:creator

• Bar-Joseph, Ziv, 1971-

Advisor dc:contributor.advisor

• David K. Gifford and Tommi S. Jaakkola.

• Electrical Engineering and Computer Science.

Statement dc:rights

• M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

en_US

Handle dc:identifier.uri

http://hdl.handle.net/1721.1/28289

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/28289