Application of neural networks to biological data mining: A case study in protein sequence classification

Document Type

Conference Proceeding

Publication Date

12-1-2000

Abstract

Biological data mining aims to extract significant information from DNA, RNA and proteins. The significant information may refer to motifs, functional sites, clustering and classification rules. This paper presents an example of biological data mining: The classification of protein sequences using neural networks. We propose new techniques to extract features from protein data and use them in combination with the Bayesian neural network to classify protein sequences obtained from the PIR protein database maintained at the National Biomedical Research Foundation. To evaluate the performance of the proposed approach, we compare it with other protein classifiers built based on sequence alignment and machine learning methods. Experimental results show the high precision of the proposed classifier and the complementarity of the tools studied in the paper.

Identifier

0034592803 (Scopus)

ISBN

[1581132336]

Publication Title

Proceeding of the Sixth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

First Page

305

Last Page

309

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