| Autor: |
C A, Kulikowski, I, Muchnik, H J, Yun, A A, Dayanik, D, Zhang, Y, Song, G T, Montelione |
| Rok vydání: |
2001 |
| Předmět: |
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| Zdroj: |
Studies in health technology and informatics. 84(Pt 2) |
| ISSN: |
0926-9630 |
| Popis: |
Domain parsing, or the detection of signals of protein structural domains from sequence data, is a complex and difficult problem. If carried out reliably it would be a powerful interpretive and predictive tool for genomic and proteomic studies. We report on a novel approach to domain parsing using consensus techniques based on Hidden Markov Models (HMMs) and BLAST searches built from a training set of 1471 continuous structural domains from the Dali Domain Dictionary (DDD). Validation on an independent test sample of family-matched structural domain sequences from the Scop database yields a consensus prediction performance rate of 75.5%, well above the 58% obtained by simple agreement of methods. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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