Computational modeling of in vivo and in vitro protein-DNA interactions by multiple instance learning.
Identifieur interne : 000E51 ( Main/Exploration ); précédent : 000E50; suivant : 000E52Computational modeling of in vivo and in vitro protein-DNA interactions by multiple instance learning.
Auteurs : Zhen Gao [États-Unis] ; Jianhua Ruan [États-Unis]Source :
- Bioinformatics (Oxford, England) [ 1367-4811 ] ; 2017.
Descripteurs français
- KwdFr :
- MESH :
English descriptors
- KwdEn :
- MESH :
- chemical , chemistry : DNA, Transcription Factors.
- chemical , metabolism : DNA, Transcription Factors.
- methods : Chromatin Immunoprecipitation, Computational Biology.
- Binding Sites, Computer Simulation, Humans, Protein Binding, Supervised Machine Learning.
Abstract
The study of transcriptional regulation is still difficult yet fundamental in molecular biology research. While the development of both in vivo and in vitro profiling techniques have significantly enhanced our knowledge of transcription factor (TF)-DNA interactions, computational models of TF-DNA interactions are relatively simple and may not reveal sufficient biological insight. In particular, supervised learning based models for TF-DNA interactions attempt to map sequence-level features ( k -mers) to binding event but usually ignore the location of k -mers, which can cause data fragmentation and consequently inferior model performance.
DOI: 10.1093/bioinformatics/btx115
PubMed: 28334224
Affiliations:
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uniqKey="Ruan J" first="Jianhua" last="Ruan">Jianhua Ruan</name><affiliation wicri:level="2"><nlm:affiliation>Department of Computer Science, University of Texas at San Antonio, San Antonio, TX, USA.</nlm:affiliation><country xml:lang="fr">États-Unis</country><wicri:regionArea>Department of Computer Science, University of Texas at San Antonio, San Antonio, TX</wicri:regionArea><placeName><region type="state">Texas</region></placeName></affiliation></author></analytic><series><title level="j">Bioinformatics (Oxford, England)</title><idno type="eISSN">1367-4811</idno><imprint><date when="2017" type="published">2017</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Binding Sites</term><term>Chromatin Immunoprecipitation (methods)</term><term>Computational Biology (methods)</term><term>Computer Simulation</term><term>DNA (chemistry)</term><term>DNA (metabolism)</term><term>Humans</term><term>Protein Binding</term><term>Supervised Machine Learning</term><term>Transcription Factors (chemistry)</term><term>Transcription Factors (metabolism)</term></keywords><keywords scheme="KwdFr" xml:lang="fr"><term>ADN ()</term><term>ADN (métabolisme)</term><term>Apprentissage machine supervisé</term><term>Biologie informatique ()</term><term>Facteurs de transcription ()</term><term>Facteurs de transcription (métabolisme)</term><term>Humains</term><term>Immunoprécipitation de la chromatine ()</term><term>Liaison aux protéines</term><term>Simulation numérique</term><term>Sites de fixation</term></keywords><keywords scheme="MESH" type="chemical" qualifier="chemistry" xml:lang="en"><term>DNA</term><term>Transcription Factors</term></keywords><keywords scheme="MESH" type="chemical" qualifier="metabolism" xml:lang="en"><term>DNA</term><term>Transcription Factors</term></keywords><keywords scheme="MESH" qualifier="methods" xml:lang="en"><term>Chromatin Immunoprecipitation</term><term>Computational Biology</term></keywords><keywords scheme="MESH" qualifier="métabolisme" xml:lang="fr"><term>ADN</term><term>Facteurs de transcription</term></keywords><keywords scheme="MESH" xml:lang="en"><term>Binding Sites</term><term>Computer Simulation</term><term>Humans</term><term>Protein Binding</term><term>Supervised Machine Learning</term></keywords><keywords scheme="MESH" xml:lang="fr"><term>ADN</term><term>Apprentissage machine supervisé</term><term>Biologie informatique</term><term>Facteurs de transcription</term><term>Humains</term><term>Immunoprécipitation de la chromatine</term><term>Liaison aux protéines</term><term>Simulation numérique</term><term>Sites de fixation</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">The study of transcriptional regulation is still difficult yet fundamental in molecular biology research. While the development of both in vivo and in vitro profiling techniques have significantly enhanced our knowledge of transcription factor (TF)-DNA interactions, computational models of TF-DNA interactions are relatively simple and may not reveal sufficient biological insight. In particular, supervised learning based models for TF-DNA interactions attempt to map sequence-level features ( k -mers) to binding event but usually ignore the location of k -mers, which can cause data fragmentation and consequently inferior model performance.</div></front></TEI><affiliations><list><country><li>États-Unis</li></country><region><li>Texas</li></region></list><tree><country name="États-Unis"><region name="Texas"><name sortKey="Gao, Zhen" sort="Gao, Zhen" uniqKey="Gao Z" first="Zhen" last="Gao">Zhen Gao</name></region><name sortKey="Ruan, Jianhua" sort="Ruan, Jianhua" uniqKey="Ruan J" first="Jianhua" last="Ruan">Jianhua Ruan</name></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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