A computational approach for the classification of protein tyrosine kinases |
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Authors: | Hyun-Chul Park Hae-Seok Eo Won Kim |
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Institution: | 1.Program in Bioinformatics,Seoul National University,Seoul,Korea;2.School of Computational Sciences,Korea Institute for Advanced Study,Seoul,Korea;3.School of Biological Sciences, College of National Sciences,Seoul National University,Seoul,Korea |
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Abstract: | Protein tyrosine kinases (PTKs) play a central role in the modulation of a wide variety of cellular events such as differentiation,
proliferation and metabolism, and their unregulated activation can lead to various diseases including cancer and diabetes.
PTKs represent a diverse family of proteins including both receptor tyrosine kinases (RTKs) and non-receptor tyrosine kinases
(NRTKs). Due to the diversity and important cellular roles of PTKs, accurate classification methods are required to better
understand and differentiate different PTKs. In addition, PTKs have become important targets for drugs, providing a further
need to develop novel methods to accurately classify this set of important biological molecules. Here, we introduce a novel
statistical model for the classification of PTKs that is based on their structural features. The approach allows for both
the recognition of PTKs and the classification of RTKs into their subfamilies. This novel approach had an overall accuracy
of 98.5% for the identification of PTKs, and 99.3% for the classification of RTKs. |
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