University of Cambridge > Talks.cam > Computational and Systems Biology > Identifying novel therapies for breast cancer using Independent Component Analysis

Identifying novel therapies for breast cancer using Independent Component Analysis

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Most standard methods for analysing tumour-derived gene expression data do not attempt to explicitly infer the altered biological processes underlying cancer. By viewing gene expression as a blind source separation (BSS) problem we can characterise the inferred biological processes in terms of aberrations in functional pathways and transcriptional programs. Using Independent Component Analysis (ICA) to perform BSS , we show that ICA significantly outperforms other linear decomposition techniques. We describe the application of ICA in a meta-analysis of breast cancer, leading to novel associations between biological pathways, regulatory modules and breast cancer phenotypes.

This talk is part of the Computational and Systems Biology series.

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