Abstract
In binary classification, it is sometimes difficult to label two training samples as negative. The aforementioned difficulty in obtaining true negative samples created a need for learning algorithms which does not use negative samples. This study aims to improve upon two PU learning algorithms, AGPS[2] and Roc-SVM[3] for protein interaction prediction. Two extensions to these algorithms is proposed; the first one is to use Random Forests as the classifier instead of support vector machines and the second is to combine the results of AGPS and Roc-SVM using a voting system. After these two approaches are implemented, their results was compared to the original algorithms as well as two well-known learning algorithms, ARACNE [9] and CLR [10]. In the comparisons, both the Random Forest (called AGPS-RF and Roc-RF) and the Hybrid algorithm performed well against the original SVM-classified ones. The improved algorithms also performed well against ARACNE and CLR.
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Pancaroglu, D., Tan, M. (2014). Improving Positive Unlabeled Learning Algorithms for Protein Interaction Prediction. In: Saez-Rodriguez, J., Rocha, M., Fdez-Riverola, F., De Paz Santana, J. (eds) 8th International Conference on Practical Applications of Computational Biology & Bioinformatics (PACBB 2014). Advances in Intelligent Systems and Computing, vol 294. Springer, Cham. https://doi.org/10.1007/978-3-319-07581-5_10
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DOI: https://doi.org/10.1007/978-3-319-07581-5_10
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