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Molecules 2018, 23(8), 2008;

A New Method for Recognizing Cytokines Based on Feature Combination and a Support Vector Machine Classifier

School of Computer Science, Inner Mongolia University, Hohhot, Inner Mongolia 010021, China
Author to whom correspondence should be addressed.
Received: 19 June 2018 / Revised: 31 July 2018 / Accepted: 7 August 2018 / Published: 11 August 2018
(This article belongs to the Special Issue Computational Analysis for Protein Structure and Interaction)
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Research on cytokine recognition is of great significance in the medical field due to the fact cytokines benefit the diagnosis and treatment of diseases, but the current methods for cytokine recognition have many shortcomings, such as low sensitivity and low F-score. Therefore, this paper proposes a new method on the basis of feature combination. The features are extracted from compositions of amino acids, physicochemical properties, secondary structures, and evolutionary information. The classifier used in this paper is SVM. Experiments show that our method is better than other methods in terms of accuracy, sensitivity, specificity, F-score and Matthew’s correlation coefficient. View Full-Text
Keywords: PSSM; PseAAC; SVM; feature combination; cytokines PSSM; PseAAC; SVM; feature combination; cytokines

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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

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Yang, Z.; Wang, J.; Zheng, Z.; Bai, X. A New Method for Recognizing Cytokines Based on Feature Combination and a Support Vector Machine Classifier. Molecules 2018, 23, 2008.

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