Publication: Identifying Dominant Amino Acid Pairs of Known Protein-Protein Interactions via K-Means Clustering
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2018-02-14
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2-s2.0-85047487395
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Mahidol University
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SCOPUS
Bibliographic Citation
Proceedings - 2017 IEEE 19th Intl Conference on High Performance Computing and Communications, HPCC 2017, 2017 IEEE 15th Intl Conference on Smart City, SmartCity 2017 and 2017 IEEE 3rd Intl Conference on Data Science and Systems, DSS 2017. Vol.2018-January, (2018), 286-291
Suggested Citation
Sudsanguan Ngamsuriyaroj, Kittirat Thepsutum Identifying Dominant Amino Acid Pairs of Known Protein-Protein Interactions via K-Means Clustering. Proceedings - 2017 IEEE 19th Intl Conference on High Performance Computing and Communications, HPCC 2017, 2017 IEEE 15th Intl Conference on Smart City, SmartCity 2017 and 2017 IEEE 3rd Intl Conference on Data Science and Systems, DSS 2017. Vol.2018-January, (2018), 286-291. doi:10.1109/HPCC-SmartCity-DSS.2017.37 Retrieved from: https://repository.li.mahidol.ac.th/handle/20.500.14594/45650
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Identifying Dominant Amino Acid Pairs of Known Protein-Protein Interactions via K-Means Clustering
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Abstract
© 2017 IEEE. Biological functions in all living cells are performed by protein-protein interactions since they form cells and control function mechanisms. Thus, identifying pairs of protein-protein interactions would be very useful, but it is not an easy task. But, doing a wet lab consumes huge amount of resources whereas using computational methods is highly challenging since they may introduce high false positives. Since a protein is a sequence of amino acids, a protein interaction would be influenced by some interactions of amino acids, and the identification of outstanding interacting pairs would give insightful meaning into how a pair of proteins interacts. This paper proposes a novel method to analyze a set of well-known protein-protein interactions for identifying a set of strong amino acid pairs that may influence the interaction. We calculate amino acid correlation values via Pearson's correlation, and use K-means clustering to group a set of outstanding amino acid pairs based on correlation values. The experimental results for 10 sets of protein interaction networks can identify a number of strong amino acid pairs among them.