Predicting genetic interactions in Caenorhabditis elegans using machine learning

The presented work develops a set of machine learning and other computational techniques to investigate and predict gene properties across a variety of biological datasets. In particular, our main goal is the discovery of genetic interactions based on sparse and incomplete information. In our development, we use gene data from two model organisms, Caenorhabditis elegans and Saccharomyces cerevisiae. Our first method, information flow, uses circuit theory to evaluate the importance of a protein in an interactome. We find that proteins with high i-flow scores mediate information exchange between functional modules. We also show that increasing information flow scores strongly correlate with the likelihood of observing lethality or pleiotropy as well as observing genetic interactions. Our metric significantly outperforms other established network metrics such as degree or betweenness. Next, we show how Bayesian sets can be applied to gain intuition as to which datasets are the most relevant for predicting genetic interactions. In order to directly apply this method to microarray data, we extend Bayesian sets to handle continuous variables. Using Bayesian sets, we show that genetically interacting genes tend to share phenotypes but are not necessarily co-localized. Additionally, they have similar development and aging temporal expression profiles. One of the major difficulties in dealing with biological data is the problem of incomplete datasets. We describe a novel application of collaborative filtering (CF) in order to predict missing values in the biological datasets.

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

2010

Author dc:creator

• Missiuro, Patrycja Vasilyev, 1976-

Advisor dc:contributor.advisor

• Tommi S. Jaakkola and Hui Ge.

• 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

eng

Handle dc:identifier.uri

http://hdl.handle.net/1721.1/57541

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/57541