Please use this identifier to cite or link to this item: https://doi.org/10.1016/j.febslet.2007.01.052
Title: VPMCD: Variable interaction modeling approach for class discrimination in biological systems
Authors: Raghuraj, R. 
Lakshminarayanan, S. 
Keywords: Computational biology
Data classification
Discriminant analysis
Machine learning
Multivariate statistics
Variable predictive models
Issue Date: 6-Mar-2007
Source: Raghuraj, R., Lakshminarayanan, S. (2007-03-06). VPMCD: Variable interaction modeling approach for class discrimination in biological systems. FEBS Letters 581 (5) : 826-830. ScholarBank@NUS Repository. https://doi.org/10.1016/j.febslet.2007.01.052
Abstract: Data classification algorithms applied for class prediction in computational biology literature are data specific and have shown varying degrees of performance. Different classes cannot be distinguished solely based on interclass distances or decision boundaries. We propose that inter-relations among the features be exploited for separating observations into specific classes. A new variable predictive model based class discrimination (VPMCD) method is described here. Three well established and proven data sets of varying statistical and biological significance are utilized as benchmark. The performance of the new method is compared with advanced classification algorithms. The new method performs better during different tests and shows higher stability and robustness. The VPMCD is observed to be a potentially strong classification approach and can be effectively extended to other data mining applications involving biological systems. © 2007 Federation of European Biochemical Societies.
Source Title: FEBS Letters
URI: http://scholarbank.nus.edu.sg/handle/10635/64787
ISSN: 00145793
DOI: 10.1016/j.febslet.2007.01.052
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