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https://olympias.lib.uoi.gr/jspui/handle/123456789/10841Full metadata record
| DC Field | Value | Language |
|---|---|---|
| dc.contributor.author | Blekas, K. | en |
| dc.contributor.author | Fotiadis, D. I. | en |
| dc.contributor.author | Likas, A. | en |
| dc.date.accessioned | 2015-11-24T17:00:56Z | - |
| dc.date.available | 2015-11-24T17:00:56Z | - |
| dc.identifier.issn | 1066-5277 | - |
| dc.identifier.uri | https://olympias.lib.uoi.gr/jspui/handle/123456789/10841 | - |
| dc.rights | Default Licence | - |
| dc.subject | protein sequence classification | en |
| dc.subject | neural networks | en |
| dc.subject | probabilistic motifs | en |
| dc.subject | meme algorithm | en |
| dc.subject | motif-based features | en |
| dc.subject | hidden markov-models | en |
| dc.subject | homologies | en |
| dc.subject | alignment | en |
| dc.subject | database | en |
| dc.subject | search | en |
| dc.title | Motif-based protein sequence classification using neural networks | en |
| heal.type | journalArticle | - |
| heal.type.en | Journal article | en |
| heal.type.el | Άρθρο Περιοδικού | el |
| heal.language | en | - |
| heal.access | campus | - |
| heal.recordProvider | Πανεπιστήμιο Ιωαννίνων. Σχολή Θετικών Επιστημών. Τμήμα Μηχανικών Ηλεκτρονικών Υπολογιστών και Πληροφορικής | el |
| heal.publicationDate | 2005 | - |
| heal.abstract | We present a system for multi-class protein classification based on neural networks. The basic issue concerning the construction of neural network systems for protein classification is the sequence encoding scheme that must be used in order to feed the neural network. To deal with this problem we propose a method that maps a protein sequence into a numerical feature space using the matching scores of the sequence to groups of conserved patterns (called motifs) into protein families. We consider two alternative ways for identifying the motifs to be used for feature generation and provide a comparative evaluation of the two schemes. We also evaluate the impact of the incorporation of background features (2-grams) on the performance of the neural system. Experimental results on real datasets indicate that the proposed method is highly efficient and is superior to other well-known methods for protein classification. | en |
| heal.journalName | Journal of Computational Biology | en |
| heal.journalType | peer reviewed | - |
| heal.fullTextAvailability | TRUE | - |
| Appears in Collections: | Άρθρα σε επιστημονικά περιοδικά ( Ανοικτά) | |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| Likas-2005-Motif-based protein sequence classification using neural networks.pdf | 349.5 kB | Adobe PDF | View/Open Request a copy |
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