Latent variable and nICA modeling of pathway gene module composite

Ting Gong; Yitan Zhu; Jianhua Xuan; Huai Li; Robert Clarke; Eric P. Hoffman; Yue Wang

doi:10.1109/IEMBS.2006.260697

Latent variable and nICA modeling of pathway gene module composite

Ting Gong, Yitan Zhu, Jianhua Xuan, Huai Li, Robert Clarke, Eric P. Hoffman, Yue Wang

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

3 Scopus citations

Abstract

In this paper, we report a new gene clustering approach - non-negative independent component analysis (nICA) - for microarray data analysis. Due to positive nature of molecular expressions, nICA fits better to the reality of corresponding putative biological processes. In conjunction with nICA model, Visual Statistical Data Analyzer (VISDA) is applied to group genes into modules in the latent variable space. The experimental results show that significant enrichment of gene annotations within clusters can be obtained.

Original language	English (US)
Title of host publication	28th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS'06
Pages	5872-5875
Number of pages	4
DOIs	https://doi.org/10.1109/IEMBS.2006.260697
State	Published - 2006
Externally published	Yes
Event	28th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS'06 - New York, NY, United States Duration: Aug 30 2006 → Sep 3 2006

Publication series

Name	Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings
ISSN (Print)	0589-1019

Other

Other	28th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS'06
Country/Territory	United States
City	New York, NY
Period	8/30/06 → 9/3/06

Keywords

Gene clustering
Latent variable model
Microarray data analysis
Module discovery
Non-negative ICA

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10.1109/IEMBS.2006.260697

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Gong, T., Zhu, Y., Xuan, J., Li, H., Clarke, R., Hoffman, E. P., & Wang, Y. (2006). Latent variable and nICA modeling of pathway gene module composite. In 28th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS'06 (pp. 5872-5875). Article 4029667 (Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings). https://doi.org/10.1109/IEMBS.2006.260697

Latent variable and nICA modeling of pathway gene module composite. / Gong, Ting; Zhu, Yitan; Xuan, Jianhua et al.
28th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS'06. 2006. p. 5872-5875 4029667 (Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Gong, T, Zhu, Y, Xuan, J, Li, H, Clarke, R, Hoffman, EP & Wang, Y 2006, Latent variable and nICA modeling of pathway gene module composite. in 28th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS'06., 4029667, Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings, pp. 5872-5875, 28th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS'06, New York, NY, United States, 8/30/06. https://doi.org/10.1109/IEMBS.2006.260697

Gong T, Zhu Y, Xuan J, Li H, Clarke R, Hoffman EP et al. Latent variable and nICA modeling of pathway gene module composite. In 28th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS'06. 2006. p. 5872-5875. 4029667. (Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings). doi: 10.1109/IEMBS.2006.260697

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abstract = "In this paper, we report a new gene clustering approach - non-negative independent component analysis (nICA) - for microarray data analysis. Due to positive nature of molecular expressions, nICA fits better to the reality of corresponding putative biological processes. In conjunction with nICA model, Visual Statistical Data Analyzer (VISDA) is applied to group genes into modules in the latent variable space. The experimental results show that significant enrichment of gene annotations within clusters can be obtained.",

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Latent variable and nICA modeling of pathway gene module composite

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