Statistical learning of gene annotations in Saccharomyces cerevisiae.
Published: 2004
Language: eng
Number of pages: 60
Description: We present machine learning algorithms for predicting the biological functions of genes. Determining function is simplified to a series of binary classifications. The main challenge of this learning task lies in the extremely small number of positives, compared with large amounts of negatives. We develop a method based on hierarchical clustering with labeled data to search for regions of high positive concentrations. We investigate logistic regression as a baseline for comparing to our technique. These methods are based on different views of the data and depending on the biological processes, one or the other of these approaches performs better, although our method makes more confident predictions for more biological processes.We also investigate data-preprocessing methods, and develop two algorithms for filling missing values in the datasets, which is necessary before some statistical methods can be used.
Notes: Adviser:  Anthony Bonner.

Thesis (M.Sc.)--University of Toronto, 2004.

Electronic version licensed for access by U. of T. users.

Source: Masters Abstracts International, Volume: 43-03, page: 0891.

MICR copy on microfiche (1 microfiche).