Identification of gene transcript signatures predictive for estrogen receptor and lymph node status using a stepwise forward selection artificial neural network modelling approach
Abstract
The advent of microarrays has attracted considerable interest from biologists due to the potential for high throughput analysis of hundreds of thousands of gene transcripts. Subsequent analysis of the data may identify specific features which correspond to characteristics of interest within the population, for example, analysis of gene expression profiles in cancer patients to identify molecular signatures corresponding with prognostic outcome. These high throughput technologies have resulted in an unprecedented rate of data generation, often of high complexity, highlighting the need for novel data analysis methodologies that will cope with data of this nature. Stepwise methods using artificial neural networks (ANNs) have been developed to identify an optimal subset of predictive gene transcripts from highly dimensional microarray data. Here these methods have been applied to a gene microarray dataset to identify and validate gene signatures corresponding with estrogen receptor and lymph node status in breast cancer. Many gene transcripts were identified whose expression could differentiate patients to very high accuracies based upon firstly whether they were positive or negative for estrogen receptor, and secondly whether meta...Continue Reading
References
Amplification and overexpression of Elongin C gene discovered in prostate cancer by cDNA microarrays
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