Global Vectors Representation of Protein Sequences and Its Application for Predicting Self-Interacting Proteins with Multi-Grained Cascade Forest Model.
Identifieur interne : 000543 ( PubMed/Checkpoint ); précédent : 000542; suivant : 000544Global Vectors Representation of Protein Sequences and Its Application for Predicting Self-Interacting Proteins with Multi-Grained Cascade Forest Model.
Auteurs : Zhan-Heng Chen [République populaire de Chine] ; Zhu-Hong You [République populaire de Chine] ; Wen-Bo Zhang [République populaire de Chine] ; Yan-Bin Wang [République populaire de Chine] ; Li Cheng [République populaire de Chine] ; Daniyal Alghazzawi [Arabie saoudite]Source :
- Genes [ 2073-4425 ] ; 2019.
Abstract
Self-interacting proteins (SIPs) is of paramount importance in current molecular biology. There have been developed a number of traditional biological experiment methods for predicting SIPs in the past few years. However, these methods are costly, time-consuming and inefficient, and often limit their usage for predicting SIPs. Therefore, the development of computational method emerges at the times require. In this paper, we for the first time proposed a novel deep learning model which combined natural language processing (NLP) method for potential SIPs prediction from the protein sequence information. More specifically, the protein sequence is de novo assembled by k-mers. Then, we obtained the global vectors representation for each protein sequences by using natural language processing (NLP) technique. Finally, based on the knowledge of known self-interacting and non-interacting proteins, a multi-grained cascade forest model is trained to predict SIPs. Comprehensive experiments were performed on yeast and human datasets, which obtained an accuracy rate of 91.45% and 93.12%, respectively. From our evaluations, the experimental results show that the use of amino acid semantics information is very helpful for addressing the problem of sequences containing both self-interacting and non-interacting pairs of proteins. This work would have potential applications for various biological classification problems.
DOI: 10.3390/genes10110924
PubMed: 31726752
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Academy of Sciences, Urumqi 830011</wicri:regionArea><wicri:noRegion>Urumqi 830011</wicri:noRegion></affiliation></author><author><name sortKey="You, Zhu Hong" sort="You, Zhu Hong" uniqKey="You Z" first="Zhu-Hong" last="You">Zhu-Hong You</name><affiliation wicri:level="1"><nlm:affiliation>The Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Urumqi 830011, China.</nlm:affiliation><country xml:lang="fr">République populaire de Chine</country><wicri:regionArea>The Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Urumqi 830011</wicri:regionArea><wicri:noRegion>Urumqi 830011</wicri:noRegion></affiliation></author><author><name sortKey="Zhang, Wen Bo" sort="Zhang, Wen Bo" uniqKey="Zhang W" first="Wen-Bo" last="Zhang">Wen-Bo Zhang</name><affiliation wicri:level="1"><nlm:affiliation>The Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Urumqi 830011, China.</nlm:affiliation><country 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Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Urumqi 830011, China.</nlm:affiliation><country xml:lang="fr">République populaire de Chine</country><wicri:regionArea>The Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Urumqi 830011</wicri:regionArea><wicri:noRegion>Urumqi 830011</wicri:noRegion></affiliation></author><author><name sortKey="Alghazzawi, Daniyal" sort="Alghazzawi, Daniyal" uniqKey="Alghazzawi D" first="Daniyal" last="Alghazzawi">Daniyal Alghazzawi</name><affiliation wicri:level="1"><nlm:affiliation>Department of Information Systems, King Abdulaziz University, Jeddah 21589, Saudi Arabia.</nlm:affiliation><country xml:lang="fr">Arabie saoudite</country><wicri:regionArea>Department of Information Systems, King Abdulaziz University, Jeddah 21589</wicri:regionArea><wicri:noRegion>Jeddah 21589</wicri:noRegion></affiliation></author></analytic><series><title level="j">Genes</title><idno type="eISSN">2073-4425</idno><imprint><date when="2019" type="published">2019</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">Self-interacting proteins (SIPs) is of paramount importance in current molecular biology. There have been developed a number of traditional biological experiment methods for predicting SIPs in the past few years. However, these methods are costly, time-consuming and inefficient, and often limit their usage for predicting SIPs. Therefore, the development of computational method emerges at the times require. In this paper, we for the first time proposed a novel deep learning model which combined natural language processing (NLP) method for potential SIPs prediction from the protein sequence information. More specifically, the protein sequence is de novo assembled by <i>k-</i><i>mers</i>. Then, we obtained the global vectors representation for each protein sequences by using natural language processing (NLP) technique. Finally, based on the knowledge of known self-interacting and non-interacting proteins, a multi-grained cascade forest model is trained to predict SIPs. Comprehensive experiments were performed on <i>yeast</i> and <i>human</i> datasets, which obtained an accuracy rate of 91.45% and 93.12%, respectively. From our evaluations, the experimental results show that the use of amino acid semantics information is very helpful for addressing the problem of sequences containing both self-interacting and non-interacting pairs of proteins. This work would have potential applications for various biological classification problems.</div></front></TEI><pubmed><MedlineCitation Status="In-Process" Owner="NLM"><PMID Version="1">31726752</PMID><DateRevised><Year>2020</Year><Month>02</Month><Day>05</Day></DateRevised><Article PubModel="Electronic"><Journal><ISSN IssnType="Electronic">2073-4425</ISSN><JournalIssue CitedMedium="Internet"><Volume>10</Volume><Issue>11</Issue><PubDate><Year>2019</Year><Month>11</Month><Day>12</Day></PubDate></JournalIssue><Title>Genes</Title><ISOAbbreviation>Genes (Basel)</ISOAbbreviation></Journal><ArticleTitle>Global Vectors Representation of Protein Sequences and Its Application for Predicting Self-Interacting Proteins with Multi-Grained Cascade Forest Model.</ArticleTitle><ELocationID EIdType="pii" ValidYN="Y">E924</ELocationID><ELocationID EIdType="doi" ValidYN="Y">10.3390/genes10110924</ELocationID><Abstract><AbstractText>Self-interacting proteins (SIPs) is of paramount importance in current molecular biology. There have been developed a number of traditional biological experiment methods for predicting SIPs in the past few years. However, these methods are costly, time-consuming and inefficient, and often limit their usage for predicting SIPs. Therefore, the development of computational method emerges at the times require. In this paper, we for the first time proposed a novel deep learning model which combined natural language processing (NLP) method for potential SIPs prediction from the protein sequence information. More specifically, the protein sequence is de novo assembled by <i>k-</i><i>mers</i>. Then, we obtained the global vectors representation for each protein sequences by using natural language processing (NLP) technique. Finally, based on the knowledge of known self-interacting and non-interacting proteins, a multi-grained cascade forest model is trained to predict SIPs. Comprehensive experiments were performed on <i>yeast</i> and <i>human</i> datasets, which obtained an accuracy rate of 91.45% and 93.12%, respectively. From our evaluations, the experimental results show that the use of amino acid semantics information is very helpful for addressing the problem of sequences containing both self-interacting and non-interacting pairs of proteins. This work would have potential applications for various biological classification problems.</AbstractText></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Chen</LastName><ForeName>Zhan-Heng</ForeName><Initials>ZH</Initials><AffiliationInfo><Affiliation>The Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Urumqi 830011, China.</Affiliation></AffiliationInfo><AffiliationInfo><Affiliation>University of Chinese Academy of Sciences, Beijing 100049, China.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>You</LastName><ForeName>Zhu-Hong</ForeName><Initials>ZH</Initials><AffiliationInfo><Affiliation>The Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Urumqi 830011, China.</Affiliation></AffiliationInfo><AffiliationInfo><Affiliation>University of Chinese Academy of Sciences, Beijing 100049, China.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Zhang</LastName><ForeName>Wen-Bo</ForeName><Initials>WB</Initials><AffiliationInfo><Affiliation>The Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Urumqi 830011, China.</Affiliation></AffiliationInfo><AffiliationInfo><Affiliation>University of Chinese Academy of Sciences, Beijing 100049, China.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Wang</LastName><ForeName>Yan-Bin</ForeName><Initials>YB</Initials><AffiliationInfo><Affiliation>The Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Urumqi 830011, China.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Cheng</LastName><ForeName>Li</ForeName><Initials>L</Initials><AffiliationInfo><Affiliation>The Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Urumqi 830011, China.</Affiliation></AffiliationInfo><AffiliationInfo><Affiliation>University of Chinese Academy of Sciences, Beijing 100049, China.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Alghazzawi</LastName><ForeName>Daniyal</ForeName><Initials>D</Initials><AffiliationInfo><Affiliation>Department of Information Systems, King Abdulaziz University, Jeddah 21589, Saudi Arabia.</Affiliation></AffiliationInfo></Author></AuthorList><Language>eng</Language><PublicationTypeList><PublicationType UI="D016428">Journal Article</PublicationType><PublicationType UI="D013485">Research Support, Non-U.S. Gov't</PublicationType></PublicationTypeList><ArticleDate DateType="Electronic"><Year>2019</Year><Month>11</Month><Day>12</Day></ArticleDate></Article><MedlineJournalInfo><Country>Switzerland</Country><MedlineTA>Genes (Basel)</MedlineTA><NlmUniqueID>101551097</NlmUniqueID><ISSNLinking>2073-4425</ISSNLinking></MedlineJournalInfo><CitationSubset>IM</CitationSubset><KeywordList Owner="NOTNLM"><Keyword MajorTopicYN="Y">de novo protein sequence</Keyword><Keyword MajorTopicYN="Y">global vector representation</Keyword><Keyword MajorTopicYN="Y">multi-grained cascade forest</Keyword><Keyword MajorTopicYN="Y">self-interacting proteins</Keyword></KeywordList><CoiStatement>The authors declare no conflict of interest.</CoiStatement></MedlineCitation><PubmedData><History><PubMedPubDate PubStatus="received"><Year>2019</Year><Month>08</Month><Day>31</Day></PubMedPubDate><PubMedPubDate PubStatus="revised"><Year>2019</Year><Month>11</Month><Day>05</Day></PubMedPubDate><PubMedPubDate PubStatus="accepted"><Year>2019</Year><Month>11</Month><Day>06</Day></PubMedPubDate><PubMedPubDate PubStatus="entrez"><Year>2019</Year><Month>11</Month><Day>16</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="pubmed"><Year>2019</Year><Month>11</Month><Day>16</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate 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