Translation initiation sites prediction with mixture Gaussian models in human cDNA sequences
Translation initiation sites (TISs) are important signals in cDNA sequences. Many research efforts have tried to predict TISs in cDNA sequences. In this paper, we propose to use mixture Gaussian models for TIS prediction. Using both local features and some features generated from global measures, th...
Gespeichert in:
| Veröffentlicht in: | IEEE transactions on knowledge and data engineering 2005-08, Vol.17 (8), p.1152-1160 |
|---|---|
| Hauptverfasser: | , , |
| Format: | Artikel |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext bestellen |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | 1160 |
|---|---|
| container_issue | 8 |
| container_start_page | 1152 |
| container_title | IEEE transactions on knowledge and data engineering |
| container_volume | 17 |
| creator | Li, G. Leong, T.-Y. Zhang, L. |
| description | Translation initiation sites (TISs) are important signals in cDNA sequences. Many research efforts have tried to predict TISs in cDNA sequences. In this paper, we propose to use mixture Gaussian models for TIS prediction. Using both local features and some features generated from global measures, the proposed method predicts TISs with a sensitivity of 98 percent and a specificity of 93.6 percent. Our method outperforms many other existing methods in sensitivity while keeping specificity high. We attribute the improvement in sensitivity to the nature of the global features and the mixture Gaussian models. |
| doi_str_mv | 10.1109/TKDE.2005.133 |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_RIE</sourceid><recordid>TN_cdi_proquest_miscellaneous_28102326</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><ieee_id>1458707</ieee_id><sourcerecordid>28102326</sourcerecordid><originalsourceid>FETCH-LOGICAL-c417t-504d5e2245f2c959b191e2dd6fb754aeb2cf61030237d06a1e584d1f641c88803</originalsourceid><addsrcrecordid>eNqFkbtPwzAQxiMEEqUwMrFEDDCl-PxInLGipSAqWMqGZLnORXWVR7ETAf89bouExADTPfS7u-_0RdE5kBEAyW8Wj5PpiBIiRsDYQTQAIWRCIYfDkBMOCWc8O45OvF8TQmQmYRC9LpxufKU72zaxbWxn96m3Hfp447CwZtd4t90qru1H1zuMZ7r33uomrtsCKx8G41Vfh9pMnsaxx7ceG4P-NDoqdeXx7DsOo5e76eL2Ppk_zx5ux_PEcMi6RBBeCKSUi5KaXOTLIBlpUaTlMhNc45KaMgXCCGVZQVINKCQvoEw5GCklYcPoer9349pw2neqtt5gVekG294rmaeUpCljgbz6k6R5RnNK5f-ghCCHpgG8_AWu29414V2VAyUZkwABSvaQca33Dku1cbbW7lMBUVvv1NY7tfVOwU7mxZ63iPjDciGzsPELquaUIg</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>912073811</pqid></control><display><type>article</type><title>Translation initiation sites prediction with mixture Gaussian models in human cDNA sequences</title><source>IEEE Electronic Library (IEL)</source><creator>Li, G. ; Leong, T.-Y. ; Zhang, L.</creator><creatorcontrib>Li, G. ; Leong, T.-Y. ; Zhang, L.</creatorcontrib><description>Translation initiation sites (TISs) are important signals in cDNA sequences. Many research efforts have tried to predict TISs in cDNA sequences. In this paper, we propose to use mixture Gaussian models for TIS prediction. Using both local features and some features generated from global measures, the proposed method predicts TISs with a sensitivity of 98 percent and a specificity of 93.6 percent. Our method outperforms many other existing methods in sensitivity while keeping specificity high. We attribute the improvement in sensitivity to the nature of the global features and the mixture Gaussian models.</description><identifier>ISSN: 1041-4347</identifier><identifier>EISSN: 1558-2191</identifier><identifier>DOI: 10.1109/TKDE.2005.133</identifier><identifier>CODEN: ITKEEH</identifier><language>eng</language><publisher>New York: IEEE</publisher><subject>Biological system modeling ; Biology computing ; classification ; DNA ; Feature extraction ; Humans ; Index Terms- Bioinformatics ; mixture Gaussian model ; Predictive models ; Proteins ; RNA ; Sequences ; Statistical analysis ; translation initiation sites</subject><ispartof>IEEE transactions on knowledge and data engineering, 2005-08, Vol.17 (8), p.1152-1160</ispartof><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2005</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c417t-504d5e2245f2c959b191e2dd6fb754aeb2cf61030237d06a1e584d1f641c88803</citedby><cites>FETCH-LOGICAL-c417t-504d5e2245f2c959b191e2dd6fb754aeb2cf61030237d06a1e584d1f641c88803</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/1458707$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>314,780,784,796,27922,27923,54756</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/1458707$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Li, G.</creatorcontrib><creatorcontrib>Leong, T.-Y.</creatorcontrib><creatorcontrib>Zhang, L.</creatorcontrib><title>Translation initiation sites prediction with mixture Gaussian models in human cDNA sequences</title><title>IEEE transactions on knowledge and data engineering</title><addtitle>TKDE</addtitle><description>Translation initiation sites (TISs) are important signals in cDNA sequences. Many research efforts have tried to predict TISs in cDNA sequences. In this paper, we propose to use mixture Gaussian models for TIS prediction. Using both local features and some features generated from global measures, the proposed method predicts TISs with a sensitivity of 98 percent and a specificity of 93.6 percent. Our method outperforms many other existing methods in sensitivity while keeping specificity high. We attribute the improvement in sensitivity to the nature of the global features and the mixture Gaussian models.</description><subject>Biological system modeling</subject><subject>Biology computing</subject><subject>classification</subject><subject>DNA</subject><subject>Feature extraction</subject><subject>Humans</subject><subject>Index Terms- Bioinformatics</subject><subject>mixture Gaussian model</subject><subject>Predictive models</subject><subject>Proteins</subject><subject>RNA</subject><subject>Sequences</subject><subject>Statistical analysis</subject><subject>translation initiation sites</subject><issn>1041-4347</issn><issn>1558-2191</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2005</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><recordid>eNqFkbtPwzAQxiMEEqUwMrFEDDCl-PxInLGipSAqWMqGZLnORXWVR7ETAf89bouExADTPfS7u-_0RdE5kBEAyW8Wj5PpiBIiRsDYQTQAIWRCIYfDkBMOCWc8O45OvF8TQmQmYRC9LpxufKU72zaxbWxn96m3Hfp447CwZtd4t90qru1H1zuMZ7r33uomrtsCKx8G41Vfh9pMnsaxx7ceG4P-NDoqdeXx7DsOo5e76eL2Ppk_zx5ux_PEcMi6RBBeCKSUi5KaXOTLIBlpUaTlMhNc45KaMgXCCGVZQVINKCQvoEw5GCklYcPoer9349pw2neqtt5gVekG294rmaeUpCljgbz6k6R5RnNK5f-ghCCHpgG8_AWu29414V2VAyUZkwABSvaQca33Dku1cbbW7lMBUVvv1NY7tfVOwU7mxZ63iPjDciGzsPELquaUIg</recordid><startdate>20050801</startdate><enddate>20050801</enddate><creator>Li, G.</creator><creator>Leong, T.-Y.</creator><creator>Zhang, L.</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. (IEEE)</general><scope>97E</scope><scope>RIA</scope><scope>RIE</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7SC</scope><scope>7SP</scope><scope>8FD</scope><scope>JQ2</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><scope>7TB</scope><scope>FR3</scope><scope>7TM</scope></search><sort><creationdate>20050801</creationdate><title>Translation initiation sites prediction with mixture Gaussian models in human cDNA sequences</title><author>Li, G. ; Leong, T.-Y. ; Zhang, L.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c417t-504d5e2245f2c959b191e2dd6fb754aeb2cf61030237d06a1e584d1f641c88803</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2005</creationdate><topic>Biological system modeling</topic><topic>Biology computing</topic><topic>classification</topic><topic>DNA</topic><topic>Feature extraction</topic><topic>Humans</topic><topic>Index Terms- Bioinformatics</topic><topic>mixture Gaussian model</topic><topic>Predictive models</topic><topic>Proteins</topic><topic>RNA</topic><topic>Sequences</topic><topic>Statistical analysis</topic><topic>translation initiation sites</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Li, G.</creatorcontrib><creatorcontrib>Leong, T.-Y.</creatorcontrib><creatorcontrib>Zhang, L.</creatorcontrib><collection>IEEE All-Society Periodicals Package (ASPP) 2005-present</collection><collection>IEEE All-Society Periodicals Package (ASPP) 1998-Present</collection><collection>IEEE Electronic Library (IEL)</collection><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>Electronics & Communications Abstracts</collection><collection>Technology Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><collection>Mechanical & Transportation Engineering Abstracts</collection><collection>Engineering Research Database</collection><collection>Nucleic Acids Abstracts</collection><jtitle>IEEE transactions on knowledge and data engineering</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Li, G.</au><au>Leong, T.-Y.</au><au>Zhang, L.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Translation initiation sites prediction with mixture Gaussian models in human cDNA sequences</atitle><jtitle>IEEE transactions on knowledge and data engineering</jtitle><stitle>TKDE</stitle><date>2005-08-01</date><risdate>2005</risdate><volume>17</volume><issue>8</issue><spage>1152</spage><epage>1160</epage><pages>1152-1160</pages><issn>1041-4347</issn><eissn>1558-2191</eissn><coden>ITKEEH</coden><abstract>Translation initiation sites (TISs) are important signals in cDNA sequences. Many research efforts have tried to predict TISs in cDNA sequences. In this paper, we propose to use mixture Gaussian models for TIS prediction. Using both local features and some features generated from global measures, the proposed method predicts TISs with a sensitivity of 98 percent and a specificity of 93.6 percent. Our method outperforms many other existing methods in sensitivity while keeping specificity high. We attribute the improvement in sensitivity to the nature of the global features and the mixture Gaussian models.</abstract><cop>New York</cop><pub>IEEE</pub><doi>10.1109/TKDE.2005.133</doi><tpages>9</tpages><oa>free_for_read</oa></addata></record> |
| fulltext | fulltext_linktorsrc |
| identifier | ISSN: 1041-4347 |
| ispartof | IEEE transactions on knowledge and data engineering, 2005-08, Vol.17 (8), p.1152-1160 |
| issn | 1041-4347 1558-2191 |
| language | eng |
| recordid | cdi_proquest_miscellaneous_28102326 |
| source | IEEE Electronic Library (IEL) |
| subjects | Biological system modeling Biology computing classification DNA Feature extraction Humans Index Terms- Bioinformatics mixture Gaussian model Predictive models Proteins RNA Sequences Statistical analysis translation initiation sites |
| title | Translation initiation sites prediction with mixture Gaussian models in human cDNA sequences |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-14T07%3A27%3A06IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_RIE&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Translation%20initiation%20sites%20prediction%20with%20mixture%20Gaussian%20models%20in%20human%20cDNA%20sequences&rft.jtitle=IEEE%20transactions%20on%20knowledge%20and%20data%20engineering&rft.au=Li,%20G.&rft.date=2005-08-01&rft.volume=17&rft.issue=8&rft.spage=1152&rft.epage=1160&rft.pages=1152-1160&rft.issn=1041-4347&rft.eissn=1558-2191&rft.coden=ITKEEH&rft_id=info:doi/10.1109/TKDE.2005.133&rft_dat=%3Cproquest_RIE%3E28102326%3C/proquest_RIE%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=912073811&rft_id=info:pmid/&rft_ieee_id=1458707&rfr_iscdi=true |