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https://olympias.lib.uoi.gr/jspui/handle/123456789/14453Full metadata record
| DC Field | Value | Language |
|---|---|---|
| dc.contributor.author | Lampros, C. | en |
| dc.contributor.author | Papaloukas, C. | en |
| dc.contributor.author | Exarchos, T. P. | en |
| dc.contributor.author | Goletsis, Y. | en |
| dc.contributor.author | Fotiadis, D. I. | en |
| dc.date.accessioned | 2015-11-24T17:38:10Z | - |
| dc.date.available | 2015-11-24T17:38:10Z | - |
| dc.identifier.issn | 0010-4825 | - |
| dc.identifier.uri | https://olympias.lib.uoi.gr/jspui/handle/123456789/14453 | - |
| dc.rights | Default Licence | - |
| dc.subject | structure prediction | en |
| dc.subject | fold recognition | en |
| dc.subject | hidden markov models | en |
| dc.subject | protein classification | en |
| dc.subject | support vector machines | en |
| dc.subject | fold recognition | en |
| dc.subject | secondary structure | en |
| dc.subject | neural networks | en |
| dc.subject | alignment | en |
| dc.title | Sequence-based protein structure prediction using a reduced state-space hidden Markov model | en |
| heal.type | journalArticle | - |
| heal.type.en | Journal article | en |
| heal.type.el | Άρθρο Περιοδικού | el |
| heal.identifier.primary | DOI 10.1016/j.compbiomed.2006.10.014 | - |
| heal.identifier.secondary | <Go to ISI>://000249489700001 | - |
| heal.identifier.secondary | http://ac.els-cdn.com/S0010482506001995/1-s2.0-S0010482506001995-main.pdf?_tid=0083e658825388d107524658e307b1b7&acdnat=1339758375_2ae10221c65f7d088804401f69608181 | - |
| heal.language | en | - |
| heal.access | campus | - |
| heal.recordProvider | Πανεπιστήμιο Ιωαννίνων. Σχολή Θετικών Επιστημών. Τμήμα Μηχανικών Επιστήμης Υλικών | el |
| heal.publicationDate | 2007 | - |
| heal.abstract | This work describes the use of a hidden Markov model (HMM), with a reduced number of states, which simultaneously learns amino acid sequence and secondary structure for proteins of known three-dimensional structure and it is used for two tasks: protein class prediction and fold recognition. The Protein Data Bank and the annotation of the SCOP database are used for training and evaluation of the proposed HMM for a number of protein classes and folds. Results demonstrate that the reduced state-space HMM performs equivalently, or even better in some cases, on classifying proteins than a HMM trained with the amino acid sequence. The major advantage of the proposed approach is that a small number of states is employed and the training algorithm is of low complexity and thus relatively fast. (C) 2006 Elsevier Ltd. All rights reserved. | en |
| heal.publisher | Elsevier | en |
| heal.journalName | Comput Biol Med | en |
| heal.journalType | peer reviewed | - |
| heal.fullTextAvailability | TRUE | - |
| Appears in Collections: | Άρθρα σε επιστημονικά περιοδικά ( Ανοικτά) | |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| Lampros-2007-Sequence-based prote.pdf | 215.51 kB | Adobe PDF | View/Open Request a copy |
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