Protein Contact Map Prediction Based on an Ensemble Learning Method
Contact map is the simplified, 2D representation of protein spatial structure. Contact map prediction is an intermediate step to predict protein 3D structure. Ensemble learning-based model is a collection of learners that is more accurate than a single learner. In this paper we propose an ensemble l...
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| creator | Habibi, N.K. Saraee, M.H. |
| description | Contact map is the simplified, 2D representation of protein spatial structure. Contact map prediction is an intermediate step to predict protein 3D structure. Ensemble learning-based model is a collection of learners that is more accurate than a single learner. In this paper we propose an ensemble learning method for contact map prediction. Results show that a considerable performance improvement is attainable using this approach instead of using a single model. |
| doi_str_mv | 10.1109/ICCET.2009.58 |
| format | Conference Proceeding |
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Contact map prediction is an intermediate step to predict protein 3D structure. Ensemble learning-based model is a collection of learners that is more accurate than a single learner. In this paper we propose an ensemble learning method for contact map prediction. Results show that a considerable performance improvement is attainable using this approach instead of using a single model.</description><identifier>ISBN: 1424433347</identifier><identifier>ISBN: 9781424433346</identifier><identifier>ISBN: 9780769535210</identifier><identifier>ISBN: 0769535216</identifier><identifier>DOI: 10.1109/ICCET.2009.58</identifier><identifier>LCCN: 2008909477</identifier><language>eng</language><publisher>IEEE</publisher><subject>Bioinformatic ; Bioinformatics ; contact map prediction ; Contacts ; Data engineering ; Database systems ; ensemble learning ; Genetic mutations ; Learning systems ; Machine learning ; neural network ; Neural networks ; Protein engineering ; Protein sequence ; protein tructure prediction</subject><ispartof>2009 International Conference on Computer Engineering and Technology, 2009, Vol.2, p.205-209</ispartof><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/4769589$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>309,310,776,780,785,786,2052,27904,54898</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/4769589$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Habibi, N.K.</creatorcontrib><creatorcontrib>Saraee, M.H.</creatorcontrib><title>Protein Contact Map Prediction Based on an Ensemble Learning Method</title><title>2009 International Conference on Computer Engineering and Technology</title><addtitle>ICCET</addtitle><description>Contact map is the simplified, 2D representation of protein spatial structure. Contact map prediction is an intermediate step to predict protein 3D structure. Ensemble learning-based model is a collection of learners that is more accurate than a single learner. In this paper we propose an ensemble learning method for contact map prediction. Results show that a considerable performance improvement is attainable using this approach instead of using a single model.</description><subject>Bioinformatic</subject><subject>Bioinformatics</subject><subject>contact map prediction</subject><subject>Contacts</subject><subject>Data engineering</subject><subject>Database systems</subject><subject>ensemble learning</subject><subject>Genetic mutations</subject><subject>Learning systems</subject><subject>Machine learning</subject><subject>neural network</subject><subject>Neural networks</subject><subject>Protein engineering</subject><subject>Protein sequence</subject><subject>protein tructure prediction</subject><isbn>1424433347</isbn><isbn>9781424433346</isbn><isbn>9780769535210</isbn><isbn>0769535216</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2009</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNotjjtPwzAYRS2hStDSkYnFfyDF78cIVqCVUtGhzJUdfwaj1qkSL_x7IsFd7tEdji5CD5RsKCX2aedce9wwQuxGmhu0pIIJwTkXeoGW82wssULrW7Sepm8yR0imrbpD7jAOFXLBbijV9xXv_RUfRoi5r3ko-MVPEPEMvuC2THAJZ8Ad-LHk8on3UL-GeI8WyZ8nWP_3Cn28tke3bbr3t5177ppMtayN8VJFTnuedGIkWeWttEkQCFFHFlRkSYWYYP6ZjI5gBOulttoE5oVmga_Q4583A8DpOuaLH39OQisrjeW_3_RJww</recordid><startdate>200901</startdate><enddate>200901</enddate><creator>Habibi, N.K.</creator><creator>Saraee, M.H.</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope></search><sort><creationdate>200901</creationdate><title>Protein Contact Map Prediction Based on an Ensemble Learning Method</title><author>Habibi, N.K. ; Saraee, M.H.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i175t-8a56d31c3f7f20f96a959f40ebd7d2b6d2f6bdfe477f87de842c57978b2a472b3</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2009</creationdate><topic>Bioinformatic</topic><topic>Bioinformatics</topic><topic>contact map prediction</topic><topic>Contacts</topic><topic>Data engineering</topic><topic>Database systems</topic><topic>ensemble learning</topic><topic>Genetic mutations</topic><topic>Learning systems</topic><topic>Machine learning</topic><topic>neural network</topic><topic>Neural networks</topic><topic>Protein engineering</topic><topic>Protein sequence</topic><topic>protein tructure prediction</topic><toplevel>online_resources</toplevel><creatorcontrib>Habibi, N.K.</creatorcontrib><creatorcontrib>Saraee, M.H.</creatorcontrib><collection>IEEE Electronic Library (IEL) Conference Proceedings</collection><collection>IEEE Proceedings Order Plan All Online (POP All Online) 1998-present by volume</collection><collection>IEEE Xplore All Conference Proceedings</collection><collection>IEEE Electronic Library (IEL)</collection><collection>IEEE Proceedings Order Plans (POP All) 1998-Present</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Habibi, N.K.</au><au>Saraee, M.H.</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Protein Contact Map Prediction Based on an Ensemble Learning Method</atitle><btitle>2009 International Conference on Computer Engineering and Technology</btitle><stitle>ICCET</stitle><date>2009-01</date><risdate>2009</risdate><volume>2</volume><spage>205</spage><epage>209</epage><pages>205-209</pages><isbn>1424433347</isbn><isbn>9781424433346</isbn><isbn>9780769535210</isbn><isbn>0769535216</isbn><abstract>Contact map is the simplified, 2D representation of protein spatial structure. Contact map prediction is an intermediate step to predict protein 3D structure. Ensemble learning-based model is a collection of learners that is more accurate than a single learner. In this paper we propose an ensemble learning method for contact map prediction. Results show that a considerable performance improvement is attainable using this approach instead of using a single model.</abstract><pub>IEEE</pub><doi>10.1109/ICCET.2009.58</doi><tpages>5</tpages></addata></record> |
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| subjects | Bioinformatic Bioinformatics contact map prediction Contacts Data engineering Database systems ensemble learning Genetic mutations Learning systems Machine learning neural network Neural networks Protein engineering Protein sequence protein tructure prediction |
| title | Protein Contact Map Prediction Based on an Ensemble Learning Method |
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