Abstract:
Independent component analysis is a well-known tool for extracting underlying mechanisms from an observed set of parallel data. Identifying such components in breast canc...Show MoreMetadata
Abstract:
Independent component analysis is a well-known tool for extracting underlying mechanisms from an observed set of parallel data. Identifying such components in breast cancer cell lines, for both copy number and gene expression, is proposed here with the goal of identifying mechanisms that affect the evolution of breast cancer in humans. This paper illustrates how to utilize independent component analysis on cell line data for achieving this goal. After the components were estimated for the well-studied chromosome 17, and then over the entire genome for a set of 14 different breast cancer cell lines, ontological analysis was performed in order to determine common gene functions that are present in each of the independent components.
Published in: 2003 IEEE XIII Workshop on Neural Networks for Signal Processing (IEEE Cat. No.03TH8718)
Date of Conference: 17-19 September 2003
Date Added to IEEE Xplore: 02 August 2004
Print ISBN:0-7803-8177-7
Print ISSN: 1089-3555