Paper Info
Title
Understanding protein structure prediction using SVM_DT
Abstract
The explanation of a decision made is important for the acceptance of machine learning technology, especially for such applications as bioinformatics. Support vector machines (SVM) have shown strong generalization ability in a number of application areas, including protein structure prediction. However, it is a black box model. On the other hand, a decision tree has good comprehensibility. In this paper, a novel approach to rule generation for understanding protein secondary structure prediction by integrating merits of both support vector machine and decision tree is presented. This approach combines SVM with decision tree into a new algorithm called SVM_DT. The results of the experiments of protein secondary structure prediction on RS126 data sets show that the comprehensibility of SVM_DT is much better than that of SVM. Moreover, the generalization ability of SVM_DT is better than that of decision tree and is similar to that of SVM. Hence, SVM_DT can be used not only for prediction, but also for guiding biological experiments.
Year
DOI
Venue
2005
10.1007/11576259_23
ISPA Workshops
Keywords
Field
DocType
protein structure prediction,novel approach,support vector machine,rs126 data set,generalization ability,protein secondary structure prediction,strong generalization ability,decision tree,application area,understanding protein structure prediction,good comprehensibility,machine learning
Protein structure prediction,Data structure,Decision tree,Binary classification,Ranking SVM,Parallel algorithm,Computer science,Support vector machine,Artificial intelligence,Black box,Machine learning
Conference
Volume
ISSN
ISBN
3759
0302-9743
3-540-29770-7
Citations 
PageRank 
References 
2
0.59
9
Authors
6
Name
Order
Citations
PageRank
Jieyue He112818.92
Hae-Jin Hu2244.47
Robert Harrison3514.58
Phang C. Tai410211.10
Yisheng Dong524520.54
Yi Pan62507203.23