Protein Secondary Structure Prediction Using Data Mining Tool C5

Authors:
Meiliu Lu;Du Zhang;Hongjun Xu;Ken Tse-yau Lau;Li Lu
Affiliations:
-;-;-;-;-
Venue:
ICTAI '99 Proceedings of the 11th IEEE International Conference on Tools with Artificial Intelligence
Year:
1999

Citing 4
Cited 1

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Abstract

This paper reports our experimental results in protein secondary structure prediction using a machine learning software C5. Accuracy improvement in prediction of protein secondary structures is the focus of our study. Starting with a target protein with unknown secondary structures, we investigate three different approaches and find that training cases selected based on sequence homology can achieve the highest predictive accuracy of 75% in test cases. Our result indicates that how to select proteins for the training cases has the most significant impact on predictive accuracy.