Some insights into protein structural class prediction

Proteins: Structure, Function and Genetics

Volumn 44, Issue 1, 2001, Pages 57-59

  Zhou, G P ^a   Assa Munt, Nuria ^a  

^a BURNHAM INSTITUTE   (United States)

Author keywords

Amino acid composition; Bayes decision rule; Component coupled algorithm; Mahalanobis distance; Protein structural class

Indexed keywords

PROTEIN;

ALGORITHM; ARTICLE; BAYES THEOREM; MATHEMATICAL ANALYSIS; PREDICTION; PRIORITY JOURNAL; PROTEIN LOCALIZATION; PROTEIN STRUCTURE; STRUCTURE ANALYSIS;

ALGORITHMS; AMINO ACIDS; BAYES THEOREM; FORECASTING; MODELS, STATISTICAL; PROTEINS;

EID: 0035400226     PISSN: 08873585     EISSN: None     Source Type: Journal    
DOI: 10.1002/prot.1071     Document Type: Article

References (13)

1. Predicting protein folding types by distance functions that make allowances for amino acid interaction
2. Prediction of protein structural classes
3. A novel approach to predicting protein structural classes in a (20-1)-D amino acid composition space
4. Understanding the recognition of protein structural classes by amino acid composition
5. Prediction and classification of domain structural classes
6. An intriguing controversy over protein structural class prediction
7. Domain structural class prediction
8. Prediction of protein secondary structure content
9. How good is prediction of protein structural class by the component-coupled method
10. The folding type of a protein is relevant to the amino acid composition
11. Protein subcellular location prediction
12. Prediction of protein structural classes by modified Mahalanobis discriminant algorithm