Gene functional classification by semi-supervised learning from heterogeneous data

Tao Li; Shenghuo Zhu; Qi Li; Mitsunori Ogihara

doi:10.1145/952532.952552

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Gene functional classification by semi-supervised learning from heterogeneous data

Tao Li, Shenghuo Zhu, Qi Li and Mitsunori Ogihara

Proceedings of the 2003 ACM symposium on applied computing, pp.78-82

SAC '03

2003-03-09

DOI: https://doi.org/10.1145/952532.952552

Abstract

minimize disagreement

semi-supervised

Support Vector Machine(SVM)

heterogeneous

gene functional classification

Gene function discovery is an important and interesting problem in computational analysis of microarray data. In this paper, we investigate the use of a semi-supervised learning algorithm for inferring gene functional classifications from heterogeneous data set consisting of DNA microarray expression measurements and phylogenetic profiles from whole-genome sequence compassions. The semisupervised learning approach aims at minimizing the disagreement between individual models built from each separate information source by employing a co-updating method and making use of both labeled and unlabeled data. Our results suggest that the semisupervised approach could be used for gene functional classification. The data sets and the program code used for the experiments can be accessed from our webpage.

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Title: Gene functional classification by semi-supervised learning from heterogeneous data
Creators: Tao Li
Shenghuo Zhu
Qi Li
Mitsunori Ogihara
Publication Details: Proceedings of the 2003 ACM symposium on applied computing, pp.78-82
Series: SAC '03
Publisher: ACM
Academic Unit: College of A&S; A&S - Computer Science
Language: English
Resource Type: Conference proceeding
Record Identifier: 991031561198102976