Xia Jingbo; Shi Feng; Hu Xuehai; Li Zhi; Song Chaohong; Xiong Huijuan

Prediction of Drought Resistance Gene with Clustered Amino Acid Features

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Author(s)

Xia Jingbo ^1,2 Shi Feng ^1,2,* Hu Xuehai ^1,2 Li Zhi ^1,2 Song Chaohong ^1,2 Xiong Huijuan ^1,2

1. College of science, Huazhong agricultural university, Wuhan, P.R. China

2. Institute of applied mathematics, Huazhong agricultural university, Wuhan, P.R. China

* Corresponding author.

DOI: https://doi.org/10.5815/ijisa.2012.11.07

Received: 13 Feb. 2012 / Revised: 4 Jun. 2012 / Accepted: 20 Aug. 2012 / Published: 8 Oct. 2012

Index Terms

Support Vector Machine, Classifier, Amino Acid Composition, K-Means

Abstract

Drought resistant gene plays important role in molecular breeding while little is known for its genetic mechanism. By extracting the clustered amino acids features, crucial numerical features are inferred for the resistance property of the given gene. Support vector machine algorithm is used to testify the reliability of feature extraction method. After carefully parameters choosing, the accuracy of the predictor achieves 79.36% in Jack-knife test, and the Mathews correlation coefficient achieves 0.5636.

Cite This Paper

Xia Jingbo, Shi Feng, Hu Xuehai, Li Zhi, Song Chaohong, Xiong Huijuan, "Prediction of Drought Resistance Gene with Clustered Amino Acid Features", International Journal of Intelligent Systems and Applications(IJISA), vol.4, no.11, pp.62-67, 2012. DOI:10.5815/ijisa.2012.11.07

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International Journal of Intelligent Systems and Applications (IJISA)