Multiclass Molecular Classification

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Author(s): Chia Huey Ooi (Duke-NUS Graduate Medical School Singapore, Singapore)
Copyright: 2009
Pages: 6
Source title: Encyclopedia of Data Warehousing and Mining, Second Edition
Source Author(s)/Editor(s): John Wang (Montclair State University, USA)
DOI: 10.4018/978-1-60566-010-3.ch209

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Abstract

Molecular classification involves the classification of samples into groups of biological phenotypes. Studies on molecular classification generally focus on cancer for the following reason: Molecular classification of tumor samples from patients into different molecular types or subtypes is vital for diagnosis, prognosis, and effective treatment of cancer (Slonim, Tamayo, Mesirov, Golub, and Lander, 2000). Traditionally, such classification relies on observations regarding the location (Slonim et al., 2000) and microscopic appearance of the cancerous cells (Garber et al., 2001). These methods have proven to be slow and ineffective; there is no way of predicting with reliable accuracy the progress of the disease, since tumors of similar appearance have been known to take different paths in the course of time. With the advent of the microarray technology, data regarding the gene expression levels in each tumor sample may now prove to be a useful tool in molecular classification. This is because gene expression data provide snapshots of the activities within the cells and thus, the profile of the state of the cells in the tissue. The use of microarrays for gene expression profiling was first published in 1995 (Schena, Shalon, Davis, and Brown, 1995). In a typical microarray experiment, the expression levels of up to 10,000 or more genes are measured in each sample. The high-dimensionality of the data means that feature selection (FS) plays a crucial role in aiding the classification process by reducing the dimensionality of the input to the classification process. In the context of FS, the terms gene and feature will be used interchangeably in the context of gene expression data.

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