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Machine Learning for Childhood Acute Lymphoblastic Leukaemia Gene Expression Data Analysis: A Review

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Among childhood cancer, acute lymphoblastic leukaemia (ALL) has been the most extensively studied propelled by the desire to improve survival rate. DNA microarray technology has expanded rapidly providing an extensive source of data that promise to pave the way for better prognosis and diagnosis of cancer and identify key targets for drug development. DNA microarray data analysis has been carried out using statistical analysis as well as machine learning and data mining approaches. In this paper, we present a comprehensive review of machine learning approaches that have been used on ALL microarray data. Followed by the research conducted by biological and medical childhood leukaemia research groups, machine learning has been used to enhance cancer diagnosis and subtype classification, development of novel therapeutic approaches and accurate identification of risk stratification of patients. These methods have been used in four major areas of microarray data analysis: gene selection, clustering, classification and pathway analysis. Each machine learning algorithm has its own advantages and drawbacks. Highlights of these as well as some outstanding future research and challenges are summarized in this paper. This review aims to serve as a starting point for those interested in microarray analysis in general and cancer research in particular.

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Keywords: Childhood acute lymphoblastic leukaemia; classification; clustering; gene regulatory networks; machine learning

Document Type: Research Article

Publication date: June 1, 2010

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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.
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