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Pavlidis, P., Weston, J., Cai, J. and Grundy, W.N. (2001) Gene functional classification from heterogeneous data. Proceedings of the 5th Annual International Conference on Computational Biology, ACM Press, Montreal, 249- 255.

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• TITLE: A comparison study between one-class and two-class machine learning for MicroRNA target detection

  AUTHORS: Malik Yousef, Naim Najami, Waleed Khalifav

  KEYWORDS: MicroRNA; One-Class; Two-Class; Machine Learning

  JOURNAL NAME: Journal of Biomedical Science and Engineering, Vol.3 No.3, April 8, 2010

  ABSTRACT: The application of one-class machine learning is gaining attention in the computational biology community. Different studies have described the use of two-class machine learning to predict microRNAs (miRNAs) gene target. Most of these methods require the generation of an artificial negative class. However, designation of the negative class can be problematic and if it is not properly done can affect the performance of the classifier dramatically and/or yield a biased estimate of performance. We present a study using one-class machine learning for miRNA target discovery and compare one-class to two-class approaches. Of all the one-class methods tested, we found that most of them gave similar accuracy that range from 0.81 to 0.89 while the two-class naive Bayes gave 0.99 accuracy. One and two class methods can both give useful classification accuracies. The advantage of one class methods is that they don’t require any additional effort for choosing the best way of generating the negative class. In these cases one- class methods can be superior to two-class methods when the features which are chosen as representative of that positive class are well defined.