Gene Expression Profile Classification: A Review

Authors: Asyali, Musa H.; Colak, Dilek; Demirkaya, Omer; Inan, Mehmet S.

Source: Current Bioinformatics, Volume 1, Number 1, January 2006 , pp. 55-73(19)

Publisher: Bentham Science Publishers

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Abstract:

In this review, we have discussed the class-prediction and discovery methods that are applied to gene expression data, along with the implications of the findings. We attempted to present a unified approach that considers both class-prediction and class-discovery. We devoted a substantial part of this review to an overview of pattern classification/recognition methods and discussed important issues such as preprocessing of gene expression data, curse of dimensionality, feature extraction/selection, and measuring or estimating classifier performance. We discussed and summarized important properties such as generalizability (sensitivity to overtraining), built-in feature selection, ability to report prediction strength, and transparency (ease of understanding of the operation) of different class-predictor design approaches to provide a quick and concise reference. We have also covered the topic of biclustering, which is an emerging clustering method that processes the entries of the gene expression data matrix in both gene and sample directions simultaneously, in detail.

Keywords: cDNA microarrays; Fisher's Linear Discriminant Analysis (FLDA); Artificial Neural Networks; multidimensional scaling; cross-validation (CV); Super-Paramagnetic Clustering algorithm

Document Type: Research article

DOI: http://dx.doi.org/10.2174/157489306775330615

Affiliations: 1: Department of Computer Engineering, Faculty of Engineering and Architecture, Yasar University, Sehitler Caddesi, 1522 Sokak, No: 6, Alsancak, Izmir, Turkey;

Publication date: 2006-01-01

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Current Bioinformatics aims to publish all the latest and outstanding developments in bioinformatics. Each issue contains a series of timely, in-depth reviews written by leaders in the field, covering a wide range of the integration of biology with computer and information science.

The journal focuses on reviews on advances in computational molecular/structural biology, encompassing areas such as computing in biomedicine and genomics, computational proteomics and systems biology, and metabolic pathway engineering. Developments in these fields have direct implications on key issues related to health care, medicine, genetic disorders, development of agricultural products, renewable energy, environmental protection, etc.

Current Bioinformatics is an essential journal for all academic and industrial researchers who want expert knowledge on all major advances in bioinformatics.

Gene Expression Profile Classification: A Review

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