HMMBinder: DNA-Binding Protein Prediction Using HMM Profile Based Features
DNA-binding proteins often play important role in various processes within the cell. Over the last decade, a wide range of classification algorithms and feature extraction techniques have been used to solve this problem. In this paper, we propose a novel DNA-binding protein prediction method called...
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| Veröffentlicht in: | BioMed research international 2017-01, Vol.2017 (2017), p.1-10 |
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| creator | Dehzangi, Abdollah Sharma, Alok Rashid, Mahmood A. Chowdhury, Shahana Yasmin Zaman, Rianon Shatabda, Swakkhar |
| description | DNA-binding proteins often play important role in various processes within the cell. Over the last decade, a wide range of classification algorithms and feature extraction techniques have been used to solve this problem. In this paper, we propose a novel DNA-binding protein prediction method called HMMBinder. HMMBinder uses monogram and bigram features extracted from the HMM profiles of the protein sequences. To the best of our knowledge, this is the first application of HMM profile based features for the DNA-binding protein prediction problem. We applied Support Vector Machines (SVM) as a classification technique in HMMBinder. Our method was tested on standard benchmark datasets. We experimentally show that our method outperforms the state-of-the-art methods found in the literature. |
| doi_str_mv | 10.1155/2017/4590609 |
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Over the last decade, a wide range of classification algorithms and feature extraction techniques have been used to solve this problem. In this paper, we propose a novel DNA-binding protein prediction method called HMMBinder. HMMBinder uses monogram and bigram features extracted from the HMM profiles of the protein sequences. To the best of our knowledge, this is the first application of HMM profile based features for the DNA-binding protein prediction problem. We applied Support Vector Machines (SVM) as a classification technique in HMMBinder. Our method was tested on standard benchmark datasets. We experimentally show that our method outperforms the state-of-the-art methods found in the literature.</description><identifier>ISSN: 2314-6133</identifier><identifier>EISSN: 2314-6141</identifier><identifier>DOI: 10.1155/2017/4590609</identifier><identifier>PMID: 29270430</identifier><language>eng</language><publisher>Cairo, Egypt: Hindawi Publishing Corporation</publisher><subject>Algorithms ; Amino Acid Sequence - genetics ; Artificial intelligence ; Bioinformatics ; Biomedical research ; Classification ; Computational Biology - methods ; Computer science ; Datasets ; Deoxyribonucleic acid ; DNA ; DNA binding proteins ; DNA-binding protein ; DNA-Binding Proteins - genetics ; Feature extraction ; Localization ; Machine learning ; Mathematical models ; Methods ; Pattern Recognition, Automated ; Physiological aspects ; Prokaryotes ; Proteins ; Support Vector Machine ; Support vector machines</subject><ispartof>BioMed research international, 2017-01, Vol.2017 (2017), p.1-10</ispartof><rights>Copyright © 2017 Rianon Zaman et al.</rights><rights>COPYRIGHT 2017 John Wiley & Sons, Inc.</rights><rights>Copyright © 2017 Rianon Zaman et al.; This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.</rights><rights>Copyright © 2017 Rianon Zaman et al. 2017</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c499t-2e202666ccba3828228bfb7dbda306d8c7e36e4fa0a585eaf8b0bb503fa33de23</citedby><cites>FETCH-LOGICAL-c499t-2e202666ccba3828228bfb7dbda306d8c7e36e4fa0a585eaf8b0bb503fa33de23</cites><orcidid>0000-0001-9919-5482 ; 0000-0003-3347-2381 ; 0000-0003-0669-072X</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC5706079/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC5706079/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,727,780,784,885,27922,27923,53789,53791</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/29270430$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><contributor>Harrison, Paul</contributor><creatorcontrib>Dehzangi, Abdollah</creatorcontrib><creatorcontrib>Sharma, Alok</creatorcontrib><creatorcontrib>Rashid, Mahmood A.</creatorcontrib><creatorcontrib>Chowdhury, Shahana Yasmin</creatorcontrib><creatorcontrib>Zaman, Rianon</creatorcontrib><creatorcontrib>Shatabda, Swakkhar</creatorcontrib><title>HMMBinder: DNA-Binding Protein Prediction Using HMM Profile Based Features</title><title>BioMed research international</title><addtitle>Biomed Res Int</addtitle><description>DNA-binding proteins often play important role in various processes within the cell. Over the last decade, a wide range of classification algorithms and feature extraction techniques have been used to solve this problem. In this paper, we propose a novel DNA-binding protein prediction method called HMMBinder. HMMBinder uses monogram and bigram features extracted from the HMM profiles of the protein sequences. To the best of our knowledge, this is the first application of HMM profile based features for the DNA-binding protein prediction problem. We applied Support Vector Machines (SVM) as a classification technique in HMMBinder. Our method was tested on standard benchmark datasets. 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Over the last decade, a wide range of classification algorithms and feature extraction techniques have been used to solve this problem. In this paper, we propose a novel DNA-binding protein prediction method called HMMBinder. HMMBinder uses monogram and bigram features extracted from the HMM profiles of the protein sequences. To the best of our knowledge, this is the first application of HMM profile based features for the DNA-binding protein prediction problem. We applied Support Vector Machines (SVM) as a classification technique in HMMBinder. Our method was tested on standard benchmark datasets. 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| subjects | Algorithms Amino Acid Sequence - genetics Artificial intelligence Bioinformatics Biomedical research Classification Computational Biology - methods Computer science Datasets Deoxyribonucleic acid DNA DNA binding proteins DNA-binding protein DNA-Binding Proteins - genetics Feature extraction Localization Machine learning Mathematical models Methods Pattern Recognition, Automated Physiological aspects Prokaryotes Proteins Support Vector Machine Support vector machines |
| title | HMMBinder: DNA-Binding Protein Prediction Using HMM Profile Based Features |
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