Gene functional classification by semi-supervised learning from heterogeneous data

Tao Li, Shenghuo Zhu, Qi Li, Mitsunori Ogihara

Research output: Contribution to conferencePaper

11 Scopus citations

Abstract

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 semi-supervised 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 semi-supervised 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.

Original languageEnglish (US)
Pages78-82
Number of pages5
DOIs
StatePublished - 2003
EventProceedings of the 2003 ACM Symposium on Applied Computing - Melbourne, FL, United States
Duration: Mar 9 2003Mar 12 2003

Other

OtherProceedings of the 2003 ACM Symposium on Applied Computing
CountryUnited States
CityMelbourne, FL
Period3/9/033/12/03

Keywords

  • Gene functional classification
  • Heterogeneous
  • Minimize disagreement
  • Semi-supervised
  • Support Vector Machine(SVM)

ASJC Scopus subject areas

  • Software

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  • Cite this

    Li, T., Zhu, S., Li, Q., & Ogihara, M. (2003). Gene functional classification by semi-supervised learning from heterogeneous data. 78-82. Paper presented at Proceedings of the 2003 ACM Symposium on Applied Computing, Melbourne, FL, United States. https://doi.org/10.1145/952532.952552