Abstract
We proposed an automated method for distinguishing cytokines from other proteins according to their primary sequences. Two strategies were employed to extract features from protein sequences. The first one is a single method, which includes autocorrelation and pseudo amino acid composition extracted feature methods based on composition and physical–chemical properties of proteins; while the second one is an optimal dimension searching method. Moreover, we developed BDSCyto as a web server to help researchers in classifying protein sequences efficiently and accurately. BDSCyto reduces the processing time and offers high accuracy by a series of efficient methods and multithreading technology based on Spark for large-scale data. Currently, numerous methods exceed 90 % accuracy in cytokine protein prediction, which is better than the existing single methods. BDSCyto is an open-source project and can be freely accessed by the public at http://bdscyto.sinaapp.com/.
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Acknowledgments
The work was supported by the Natural Science Foundation of China (No. 61370010, 61572384, 61402545).
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© 2016 Springer International Publishing Switzerland
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Zou, Q., Wan, S., Han, B., Zhan, Z. (2016). BDSCyto: An Automated Approach for Identifying Cytokines Based on Best Dimension Searching. In: Booth, R., Zhang, ML. (eds) PRICAI 2016: Trends in Artificial Intelligence. PRICAI 2016. Lecture Notes in Computer Science(), vol 9810. Springer, Cham. https://doi.org/10.1007/978-3-319-42911-3_60
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DOI: https://doi.org/10.1007/978-3-319-42911-3_60
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