NDLI: Prediction of disease-resistant gene in rice based on SVM-RFE

Content Provider	IEEE Xplore Digital Library
Author	Yanjiao Ren Deping Wang Yan Wang Jinbo Zhou Hanyuan Zhang You Zhou Yanchun Liang
Copyright Year	2010
Description	Author affiliation: College of Computer Science and Technology, Jilin University, Changchun 130012, China (Yanjiao Ren; Yan Wang; Jinbo Zhou; Hanyuan Zhang; You Zhou; Yanchun Liang) \|\| Department of Advanced Biotechnology, China National Center for Biotechnology Development, Beijing 100036, China (Deping Wang)
Abstract	Gene expression data possess two main features: small samples and high dimensions. There are many difficulties on analyzing gene expression data using the traditional machine learning methods. In this paper we use an SVM-RFE based method to obtain the set of trait genes that are related to the disease-resistance property in rice and evaluate these genes according to some heuristics. And then we query the genes in the GEO database whose query results in turn provide us with the important rice disease resistance and disease susceptible gene IDs. Therefore, we can determine the rice genes that cause the rice to grow normally and illy. The method not only reduces the dimensionality and improves the classification accuracy substantially compared to other classification methods, but also carries significant biological implications.
Starting Page	2343
Ending Page	2346
File Size	308254
Page Count	4
File Format	PDF
ISBN	9781424464951
e-ISBN	9781424464982
DOI	10.1109/BMEI.2010.5640583
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-10-16
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Support vector machines Proteins disease resistance gene Accuracy SVM-RFE Classification algorithms Gene expression feature selection Immune system Diseases
Content Type	Text
Resource Type	Article

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