@inproceedings{f14fb39c8d094edea58d412e54e978ef,
title = "Latent variable and nICA modeling of pathway gene module composite",
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.",
keywords = "Gene clustering, Latent variable model, Microarray data analysis, Module discovery, Non-negative ICA",
author = "Ting Gong and Yitan Zhu and Jianhua Xuan and Huai Li and Robert Clarke and Hoffman, {Eric P.} and Yue Wang",
year = "2006",
doi = "10.1109/IEMBS.2006.260697",
language = "English (US)",
isbn = "1424400325",
series = "Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings",
pages = "5872--5875",
booktitle = "28th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS'06",
note = "28th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS'06 ; Conference date: 30-08-2006 Through 03-09-2006",
}