Functional classification of proteins by pattern discovery and top-down clustering of primary sequences

IBM Systems Journal

Volumn 40, Issue 2, 2001, Pages 379-393

  Liu, A H ^a   Califano, A ^a  

^a IBM T J WATSON RESEARCH CENTER   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; DATABASE SYSTEMS; MARKOV PROCESSES; MATHEMATICAL MODELS; MUTAGENESIS; STATISTICAL METHODS;

CLUSTERING TECHNIQUES;

PROTEINS;

EID: 0034958873     PISSN: 00188670     EISSN: None     Source Type: Journal    
DOI: 10.1147/sj.402.0379     Document Type: Article

References (37)

1. Systematic and automated discovery of patterns in PROSITE families
2. SPLASH: Structural pattern localization and analysis by sequential histograms
3. The PROSITE dictionary of sites and patterns in proteins, its current status
4. A map of the protein space - An automatic hierarchical classification of all protein sequences
5. Profile analysis
6. Hidden Markov models in computational biology: Applications to protein modeling
7. A genomic perspective on protein families
8. Automated protein sequence database classification: I. Integration of compositional similarity search, local similarity search, and multiple sequence alignment
9. Automated protein sequence database classification: II. Delineation of domain boundaries from sequence similarities
10. Computationally efficient cluster representation in molecular sequence megaclassification
11. Modular arrangement of proteins as inferred from analysis of homology
12. Dictionary building via unsupervised hierarchical motif discovery in the sequence space of natural proteins
13. GPCRDB: An information system for G protein-coupled receptors
14. GCRDb: A G protein-coupled receptor database
15. PRINTS - A database of protein motif fingerprints
16. Molecular architecture of G protein-coupled receptors
17. Matrices for detecting distant relationships
18. Amino acid substitution matrices from protein blocks
19. Mixtures of Dirichlet processes with applications to Bayesian nonparametric problems
20. Using Dirichlet mixture priors to derive hidden Markov models for protein families
21. Strong limit theorems of empirical functionals for large exceedances of partial sums of I.I.D. variables
22. Statistical composition of high-scoring segments from molecular sequences
23. Methods for assessing the statistical significance of molecular sequence features by using general scoring schemes
24. WHAT IF: A molecular modeling and drug program
25. Classification and identification of proteins by means of common and specific amino acid N-tuples in unaligned sequences
26. The α and ω of G-protein coupled receptors - A novel method for classification, Part 1
27. The α and ω of G-protein coupled receptors: A novel method for classification. Part 2: Bin classification
28. The SBASE protein domain library, release 7.0: A collection of annotated protein sequence segments
29. Pfam: A comprehensive database of protein domain families based on seed alignments
30. Automatic generation of primary sequence patterns from sets of related protein sequences