Abstract
A comparative study for assessing the reliability of protein-protein interactions in a high-throughput dataset is presented. We use various state-of-the-art classification algorithms to distinguish true interacting protein pairs from noisy data using the empirical knowledge about interacting proteins. Then we compare the performance of classifiers with various criteria. Experimental results show that classification algorithms provide very powerful tools in distinguishing true interacting protein pairs from noisy protein-protein interaction dataset. Furthermore, in the data setting with lots of missing values like protein-protein interaction dataset, K-Nearest Neighborhood and Decision Tree algorithms show best performance among other methods.
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Lee, M.S., Park, S.S. (2006). Comparative Analysis of Classification Methods for Protein Interaction Verification System. In: Yakhno, T., Neuhold, E.J. (eds) Advances in Information Systems. ADVIS 2006. Lecture Notes in Computer Science, vol 4243. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11890393_24
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DOI: https://doi.org/10.1007/11890393_24
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-46291-0
Online ISBN: 978-3-540-46292-7
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