Abstract
Accuracy of disease classification has always been a challenging goal of bioinformatics research. Microarray-based classification of disease states relies on the use of gene expression profiles of patients to identify those that have profiles differing from the control group. A number of methods have been proposed to identify diagnostic markers that can accurately discriminate between different classes of a disease. Pathway-based microarray analysis for disease classification can help improving the classification accuracy. The experimental results showed that the use of pathway activities inferred by the negatively correlated feature sets (NCFS) based methods achieved higher accuracy in disease classification than other different pathway-based feature selection methods for two breast cancer metastasis datasets.
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Sootanan, P., Meechai, A., Prom-on, S., Chan, J.H. (2011). Pathway-Based Microarray Analysis with Negatively Correlated Feature Sets for Disease Classification. In: Lu, BL., Zhang, L., Kwok, J. (eds) Neural Information Processing. ICONIP 2011. Lecture Notes in Computer Science, vol 7062. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24955-6_80
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DOI: https://doi.org/10.1007/978-3-642-24955-6_80
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