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The Application of Support Vector Machines on Medical and Bioinformatics

Author: WangYi
Tutor: WanFuYong
School: East China Normal University
Course: Operational Research and Cybernetics
Keywords: Support Vector Machine The diagnosis of breast cancer MicroRNA Cross-entropy Filter Information Biology
CLC: R318.6
Type: Master's thesis
Year: 2007
Downloads: 174
Quote: 2
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The support vector machine (SVM) is a data mining of the 1990s developed a new method , it showed very good results in many areas of practical application . The main work of this paper is the successful application of SVM two . On the diagnosis of breast cancer , support vector machine classifier with good generalization . We use the asymmetric punishment of C-SVM to solve the problem of asymmetric number of positive and negative class samples ; based generalization sector with fast parameter search method , this method than simply k -fold cross- validation search parameters faster . Use the cross-entry filter feature selection , we get a better predictive accuracy . SVM other applications are prediction plant microRNA precursors . microRNAs (miRNAs) are a class of non - protein-coding small molecule RNA of about 22 bases in length , has played a very important role in the growth and development of multi-cellular organisms . In this research , we have developed a whole new SVM classifier is used to search for plants miNRA precursor . This classification model used on behalf of the precursor 12 global and sub- structure characteristics , training of 790 positive class samples and 7900 negative class samples , model half of the amount of accuracy rate is 96.43% . To test the newly discovered 53 plant miRNA precursors (n- type) and of 62,883 negative class SVM classifier accuracy of 99.85% , a sensitivity of 79.25% and 99.87% specificity . Very good specificity makes the method applied to the genome level prediction of plant miRNA genes become possible , and this method can only use a single genome sequence information to predict which species-specific miRNA genes will be found to provide a very effective tool .

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