Transmembrane segments prediction and understanding using support vector machine and decision tree

In recent years, there have been many studies focusing on improving the accuracy of prediction of transmembrane segments, and many significant results have been achieved. In spite of these considerable results, the existing methods lack the ability to explain the process of how a learning result is reached and why a prediction decision is made. The explanation of a decision made is important for the acceptance of machine learning technology in bioinformatics applications such as protein structure prediction. While support vector machines (SVM) have shown strong generalization ability in a number of application areas, including protein structure prediction, they are black box models and hard to understand. On the other hand, decision trees provide insightful interpretation, however, they have lower prediction accuracy. In this paper, we present an innovative approach to rule generation for understanding prediction of transmembrane segments by integrating the merits of both SVMs and dec...

• Type: Journal
• Year: 2006
• Where: ESWA
• Authors: Jieyue He, Hae-Jin Hu, Robert W. Harrison, Phang C. Tai, Yi Pan
• Keywords: ESWA 2006, Prediction, Protein Structure Prediction, Transmembrane Segments