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Data-Driven Generation of Decision Trees for Motif-based Assignment of Protein Sequences to Functional Families


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Wang, Dake, Wang, Xiangyun, Dobbs, Drena and Honavar, Vasant (2000) Data-Driven Generation of Decision Trees for Motif-based Assignment of Protein Sequences to Functional Families. Technical Report TR00-14a, Department of Computer Science, Iowa State University.

Full text available as:Postscript

Abstract

Data-Driven Generation of decision Trees for Motif-Based Assignment of
Protein Sequences to Functional Families. Technical Report ISU-CS-TR
00-14. Dake Wang*, Xiangyun Wang*, Drena Dobbs+ and Vasant Honavar*,
*Artificial Intelligence Research Laboratory, Department of Computer
Science, +Department of Zoology and Genetics and Graduate Program in
Bioinformatics and Computational Biology, Iowa State University, Ames,
IA50010, USA. Email: wangd, xyunwang, honavar@cs.iastate.edu,
d_dobbs@molebio.iastate.edu Keywords: Decision Trees, Motifs, Protein
Classification, Functional Families, Function Prediction, Intelligent
Agents, Data-Driven Process, Functional Genomics.
Abstract: This paper describes an approach to data-driven discovery of
sequence motif-based models in the form of decision trees for assigning
protein sequences to functional families. Experiments using several
protein data sets indicate that proposed approach matches or beats the
technique of assigning protein sequences to functional families based on
the presence of a single characteristic motif in terms of the accuracy of
resulting classification.

Subjects:All uncategorized technical reports
ID code:00000229
Deposited by:Staff Account on 03 August 2000



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