Bayesian classification of protein structure

IEEE Expert-Intelligent Systems and their Applications

Volumn 7, Issue 4, 1992, Pages 67-75

  Hunter, Lawrence ^a   States, David J ^a  

^a NATIONAL INSTITUTES OF HEALTH   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 0038297535     PISSN: 08859000     EISSN: None     Source Type: Journal    
DOI: 10.1109/64.153466     Document Type: Article

References (10)

1. W. Kabsch and C. Sander, “Dictionary of Protein Secondary Structure: Pattern Recognition of Hydrogen-Bonded and Geometrical Features,” Biopolymers, Vol. 22, No. 12, Dec. 1983, pp. 2,577-2,637.
2. L. Hunter, “Knowledge Acquisition Planning for Inference from Large Data Sets, Proc. 23rdAnn. Hawaii Int'l Conf. on System Sciences, Vol. 2, IEEE Computer Society Press, Los Alamitos, Calif., 1990, pp. 35–44.
3. L. Hunter, “Planning to Learn,” Proc. 12th Ann. Conf. Cognitive Science Soc., Lawrence Erlbaum Associates, Hillsdale, N. J., pp. 26–34.
4. M. Rooman, J. Rodriguez, and S. Wodak, “Automatic Definition of Recurrent Local Structure Motifs in Proteins,” J. Molecular Biology, Vol. 213, No. 2, May 20,1990, pp. 327–336.
5. R. Unger et al., “A 3D-Building-Blocks Approach to Analyzing and Predicting Structure of Proteins,” Proteins, Vol. 5, No. 4, 1989, pp. 355–373.
6. S. Prestrelski, Experimental and Computational Approaches to Protein Secondary Structure, doctoral dissertation, City Univ. of New York, Dept, of Biomedical Sci., 1990.
7. D. Fisher, “Knowledge Acquisition via Incremental Conceptual Clustering,”Machine Learning, Vol. 2, No. 2, Sept. 1987, pp. 139–172.
8. L. Hunter, “Artificial Intelligence and Molecular Biology,” AI Magazine, Vol. 11, No. 5, Spring 1991, pp. 27–36.
9. P. Cheeseman et al., “Autoclass: A Bayesian Classification System,” Proc. Fifth Int’l Conf. Machine Learning, Morgan Kauf-mann, San Mateo, Calif, 1988.
10. G. Rose, W. Young, and L. Gierasch, “In-terior Turns in Globular Proteins,” Nature, Vol. 304, No. 5927, Aug. 18–24, 1983, pp. 654–657.