A Four-Layer Reference Model for Prediction of Protein Side-Chains Based on Dynamic Bayesian Networks
A four-layer reference model for prediction of protein side-chains based on Dynamic Bayesian Networks is proposed. The relation of backbone with four side chain torsion angles is considered in this model, and the problem caused by sparse data is resolved to a certain extent. Experiment shows that th...
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| creator | Xu Huang Qiang Lv Dajun Miao Peide Qian |
| description | A four-layer reference model for prediction of protein side-chains based on Dynamic Bayesian Networks is proposed. The relation of backbone with four side chain torsion angles is considered in this model, and the problem caused by sparse data is resolved to a certain extent. Experiment shows that this method has produced a better DBN model and more exact prediction results. It indicates that the four-layer reference model is effective. |
| doi_str_mv | 10.1109/ISCID.2012.193 |
| format | Conference Proceeding |
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The relation of backbone with four side chain torsion angles is considered in this model, and the problem caused by sparse data is resolved to a certain extent. Experiment shows that this method has produced a better DBN model and more exact prediction results. 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The relation of backbone with four side chain torsion angles is considered in this model, and the problem caused by sparse data is resolved to a certain extent. Experiment shows that this method has produced a better DBN model and more exact prediction results. It indicates that the four-layer reference model is effective.</description><subject>Amino acids</subject><subject>Bayesian methods</subject><subject>Data models</subject><subject>Dynamic Bayesian Networks</subject><subject>Hidden Markov models</subject><subject>Prediction of Protein Structure</subject><subject>Prediction of Side-chain</subject><subject>Predictive models</subject><subject>Proteins</subject><subject>Training</subject><isbn>1467326461</isbn><isbn>9781467326469</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2012</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNotj11LwzAYhQMy0M3deuNN_kBrvpo0l7NzWqgfOL0eWfIGo1sjSUX6763o1eE8Dxw4CF1QUlJK9FW7bdp1yQhlJdX8BM2pkIozKSSdofkv14IJpk_RMud3QgjVUknOzhCs8CZ-paIzIyT8DB4S9BbwfXRwwD4m_JTABTuE2OPopxYHCD3eBgdF82ZCn_G1yeDw5Ndjb47BTmCEHEyPH2D4jukjn6OZN4cMy_9coNfNzUtzV3SPt22z6opAVTUUjFluOHgOTjgHejrga0W9thxqDZzWvgLJqXOWy30tPLdKuz0w4ZRylPEFuvzbDQCw-0zhaNK4k4JUlWb8BxTqVic</recordid><startdate>201210</startdate><enddate>201210</enddate><creator>Xu Huang</creator><creator>Qiang Lv</creator><creator>Dajun Miao</creator><creator>Peide Qian</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope></search><sort><creationdate>201210</creationdate><title>A Four-Layer Reference Model for Prediction of Protein Side-Chains Based on Dynamic Bayesian Networks</title><author>Xu Huang ; Qiang Lv ; Dajun Miao ; Peide Qian</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i175t-22c3a3ef3ed4dde9264f871f9c3e89e318f5e631ddc36b84f3c79dbe24d77d123</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2012</creationdate><topic>Amino acids</topic><topic>Bayesian methods</topic><topic>Data models</topic><topic>Dynamic Bayesian Networks</topic><topic>Hidden Markov models</topic><topic>Prediction of Protein Structure</topic><topic>Prediction of Side-chain</topic><topic>Predictive models</topic><topic>Proteins</topic><topic>Training</topic><toplevel>online_resources</toplevel><creatorcontrib>Xu Huang</creatorcontrib><creatorcontrib>Qiang Lv</creatorcontrib><creatorcontrib>Dajun Miao</creatorcontrib><creatorcontrib>Peide Qian</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>Xu Huang</au><au>Qiang Lv</au><au>Dajun Miao</au><au>Peide Qian</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>A Four-Layer Reference Model for Prediction of Protein Side-Chains Based on Dynamic Bayesian Networks</atitle><btitle>2012 Fifth International Symposium on Computational Intelligence and Design</btitle><stitle>iscid</stitle><date>2012-10</date><risdate>2012</risdate><volume>2</volume><spage>165</spage><epage>169</epage><pages>165-169</pages><isbn>1467326461</isbn><isbn>9781467326469</isbn><coden>IEEPAD</coden><abstract>A four-layer reference model for prediction of protein side-chains based on Dynamic Bayesian Networks is proposed. The relation of backbone with four side chain torsion angles is considered in this model, and the problem caused by sparse data is resolved to a certain extent. Experiment shows that this method has produced a better DBN model and more exact prediction results. It indicates that the four-layer reference model is effective.</abstract><pub>IEEE</pub><doi>10.1109/ISCID.2012.193</doi><tpages>5</tpages></addata></record> |
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| source | IEEE Electronic Library (IEL) Conference Proceedings |
| subjects | Amino acids Bayesian methods Data models Dynamic Bayesian Networks Hidden Markov models Prediction of Protein Structure Prediction of Side-chain Predictive models Proteins Training |
| title | A Four-Layer Reference Model for Prediction of Protein Side-Chains Based on Dynamic Bayesian Networks |
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