Gene functional classification by semi-supervised learning from heterogeneous data

Tao Li, Shenghuo Zhu, Qi Li, Mitsunori Ogihara

Research output: Chapter in Book/Report/Conference proceedingConference contribution

11 Citations (Scopus)

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)
Title of host publicationProceedings of the ACM Symposium on Applied Computing
EditorsG. Lamont
Pages78-82
Number of pages5
StatePublished - 2003
Externally publishedYes
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

Fingerprint

Supervised learning
Genes
Microarrays
Learning algorithms
DNA
Experiments

Keywords

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

ASJC Scopus subject areas

  • Computer Science(all)

Cite this

Li, T., Zhu, S., Li, Q., & Ogihara, M. (2003). Gene functional classification by semi-supervised learning from heterogeneous data. In G. Lamont (Ed.), Proceedings of the ACM Symposium on Applied Computing (pp. 78-82)

Gene functional classification by semi-supervised learning from heterogeneous data. / Li, Tao; Zhu, Shenghuo; Li, Qi; Ogihara, Mitsunori.

Proceedings of the ACM Symposium on Applied Computing. ed. / G. Lamont. 2003. p. 78-82.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Li, T, Zhu, S, Li, Q & Ogihara, M 2003, Gene functional classification by semi-supervised learning from heterogeneous data. in G Lamont (ed.), Proceedings of the ACM Symposium on Applied Computing. pp. 78-82, Proceedings of the 2003 ACM Symposium on Applied Computing, Melbourne, FL, United States, 3/9/03.
Li T, Zhu S, Li Q, Ogihara M. Gene functional classification by semi-supervised learning from heterogeneous data. In Lamont G, editor, Proceedings of the ACM Symposium on Applied Computing. 2003. p. 78-82
Li, Tao ; Zhu, Shenghuo ; Li, Qi ; Ogihara, Mitsunori. / Gene functional classification by semi-supervised learning from heterogeneous data. Proceedings of the ACM Symposium on Applied Computing. editor / G. Lamont. 2003. pp. 78-82
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