DocumentCode :
394146
Title :
Diseases classification using support vector machine (SVM)
Author :
Sheng, Liu ; Qing, Song ; Wenjie, Hu ; Aize, Cao
Author_Institution :
Sch. of Electr. & Electron. Eng., Nanyang Technol. Univ., Singapore
Volume :
2
fYear :
2002
fDate :
18-22 Nov. 2002
Firstpage :
760
Abstract :
The paper proposed a new method: disease classification based on protein sequence. Support vector machine was used for this problem and a new encoding for the multicode protein sequence was suggested. Two extracted features were selected for classifying, the results showed the capability of SVM for such bioinformatics problems and the goodness of the system of protein sequence based disease classification. It gave error around 4-5%, which presented that although it is good for such problems, improvement of the algorithm also should be made.
Keywords :
biology computing; diseases; feature extraction; medical computing; proteins; support vector machines; SVM; bioinformatics problems; disease classification; multicode protein sequence; protein sequence; support vector machine; Bioinformatics; Biological information theory; Data mining; Diseases; Genomics; Machine learning; Protein sequence; Sequences; Support vector machine classification; Support vector machines;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Neural Information Processing, 2002. ICONIP '02. Proceedings of the 9th International Conference on
Print_ISBN :
981-04-7524-1
Type :
conf
DOI :
10.1109/ICONIP.2002.1198160
Filename :
1198160
Link To Document :
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