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A new multitask learning method for multiorganism gene network estimation

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dc.contributor.author Nassar M.
dc.contributor.author Abdallah R.
dc.contributor.author Zeineddine H.A.
dc.contributor.author Yaacoub E.
dc.contributor.author Dawy Z.
dc.contributor.editor
dc.date 2008
dc.date.accessioned 2017-09-07T07:07:20Z
dc.date.available 2017-09-07T07:07:20Z
dc.date.issued 2008
dc.identifier 10.1109/ISIT.2008.4595398
dc.identifier.isbn 9.7814244226e+012
dc.identifier.issn 21578101
dc.identifier.uri http://hdl.handle.net/10938/11339
dc.description.abstract A new method for multitask learning in a Bayesian network context is presented for multiorganism gene network estimation. When the input datasets are sparse, as is the case in microarray gene expression data, it becomes difficult to separate random correlations from actual edges in the true underlying Bayesian network. Multitask learning takes advantage of the similarity between related tasks, in order to construct a more accurate model of the underlying relationships represented by the Bayesian networks. The proposed method is tested on synthetic data to illustrate its validity. Then it is iteratively applied on real gene expression data to learn the genetic regulatory networks of two organisms with homologous genes (human and yeast). © 2008 IEEE.
dc.format.extent
dc.format.extent Pages: (2287-2291)
dc.language English
dc.publisher NEW YORK
dc.relation.ispartof Publication Name: IEEE International Symposium on Information Theory - Proceedings; Conference Title: 2008 IEEE International Symposium on Information Theory, ISIT 2008; Conference Date: 6 July 2008 through 11 July 2008; Conference Location: Toronto, ON; Publication Year: 2008; Pages: (2287-2291);
dc.relation.ispartofseries
dc.relation.uri
dc.source Scopus
dc.subject.other
dc.title A new multitask learning method for multiorganism gene network estimation
dc.type Conference Paper
dc.contributor.affiliation Nassar, M., Electrical and Computer Engineering Department, American University of Beirut, Riad El-Solh P.O. Box 11-0236, Beirut 1107 2020, Lebanon
dc.contributor.affiliation Abdallah, R., Electrical and Computer Engineering Department, American University of Beirut, Riad El-Solh P.O. Box 11-0236, Beirut 1107 2020, Lebanon
dc.contributor.affiliation Zeineddine, H.A., Electrical and Computer Engineering Department, American University of Beirut, Riad El-Solh P.O. Box 11-0236, Beirut 1107 2020, Lebanon
dc.contributor.affiliation Yaacoub, E., Electrical and Computer Engineering Department, American University of Beirut, Riad El-Solh P.O. Box 11-0236, Beirut 1107 2020, Lebanon
dc.contributor.affiliation Dawy, Z., Electrical and Computer Engineering Department, American University of Beirut, Riad El-Solh P.O. Box 11-0236, Beirut 1107 2020, Lebanon
dc.contributor.authorAddress Nassar, M.; Electrical and Computer Engineering Department, American University of Beirut, Riad El-Solh P.O. Box 11-0236, Beirut 1107 2020, Lebanon; email: mhn04@aub.edu.lb
dc.contributor.authorCorporate University: American University of Beirut; Faculty: Faculty of Engineering and Architecture; Department: Electrical and Computer Engineering;
dc.contributor.authorDepartment Electrical and Computer Engineering
dc.contributor.authorDivision
dc.contributor.authorEmail mhn04@aub.edu.lb; raa57@aub.edu.lb; hma4l@aub.edu.lb; eey00@aub.edu.lb; zaher.dawy@aub.edu.lb
dc.contributor.authorFaculty Faculty of Engineering and Architecture
dc.contributor.authorInitials Nassar, M
dc.contributor.authorInitials Abdallah, R
dc.contributor.authorInitials Zeineddine, HA
dc.contributor.authorInitials Yaacoub, E
dc.contributor.authorInitials Dawy, Z
dc.contributor.authorOrcidID
dc.contributor.authorReprintAddress Nassar, M (reprint author), Amer Univ Beirut, Dept Elect and Comp Engn, POB 11-0236, Beirut 11072020, Lebanon.
dc.contributor.authorResearcherID
dc.contributor.authorUniversity American University of Beirut
dc.description.cited BERGMANN S, 2004, PLOS BIOL, V2; BRUN M, 2007, EURASIP J BIOINF JAN; Caruana R, 1997, MACH LEARN, V28, P41, DOI 10.1023-A:1007379606734; Friedman N, 2000, J COMPUT BIOL, V7, P601, DOI 10.1089-106652700750050961; JIMENEZ JL, 2003, GENOME BIOL, V4; Kanehisa M, 2006, NUCLEIC ACIDS RES, V34, P354, DOI DOI 10.1093-NAR-GKJ102; NEAPOLITAN E, 2004, LEARNING BAYESIAN NE; NICULESCUMIZIL A, 2005, P IND TRANSF NIPS WO; Spellman PT, 1998, MOL BIOL CELL, V9, P3273; Tamada Yoshinori, 2005, Journal of Bioinformatics and Computational Biology, V3, P1295, DOI 10.1142-S0219720005001569; Whitfield ML, 2002, MOL BIOL CELL, V13, P1977, DOI 10.1091-mbc.02-02-0030; Xue Y, 2007, J MACH LEARN RES, V8, P35
dc.description.citedCount
dc.description.citedTotWOSCount 0
dc.description.citedWOSCount 0
dc.format.extentCount 5
dc.identifier.articleNo 4595398
dc.identifier.coden PISTF
dc.identifier.pubmedID
dc.identifier.scopusID 52349104537
dc.identifier.url
dc.publisher.address 345 E 47TH ST, NEW YORK, NY 10017 USA
dc.relation.ispartofConference Conference Title: 2008 IEEE International Symposium on Information Theory, ISIT 2008 : Conference Date: 6 July 2008 through 11 July 2008 , Conference Location: Toronto, ON.
dc.relation.ispartofConferenceCode 73582
dc.relation.ispartofConferenceDate 6 July 2008 through 11 July 2008
dc.relation.ispartofConferenceHosting
dc.relation.ispartofConferenceLoc Toronto, ON
dc.relation.ispartofConferenceSponsor IEEE, Information Theory Society
dc.relation.ispartofConferenceTitle 2008 IEEE International Symposium on Information Theory, ISIT 2008
dc.relation.ispartofFundingAgency
dc.relation.ispartOfISOAbbr
dc.relation.ispartOfIssue
dc.relation.ispartOfPart
dc.relation.ispartofPubTitle IEEE International Symposium on Information Theory - Proceedings
dc.relation.ispartofPubTitleAbbr IEEE Int Symp Inf Theor Proc
dc.relation.ispartOfSpecialIssue
dc.relation.ispartOfSuppl
dc.relation.ispartOfVolume
dc.source.ID WOS:000260364401181
dc.type.publication Book
dc.subject.otherAuthKeyword Bayesian networks
dc.subject.otherAuthKeyword Evolutionary information
dc.subject.otherAuthKeyword Genetic regulatory networks
dc.subject.otherAuthKeyword Multitask learning
dc.subject.otherChemCAS
dc.subject.otherIndex Bayesian
dc.subject.otherIndex Data-sets
dc.subject.otherIndex Evolutionary information
dc.subject.otherIndex Gene expression data
dc.subject.otherIndex Gene network
dc.subject.otherIndex Genetic regulatory networks
dc.subject.otherIndex Homologous genes
dc.subject.otherIndex International symposium
dc.subject.otherIndex Microarray gene expression data
dc.subject.otherIndex Multitask learning
dc.subject.otherIndex Synthetic data
dc.subject.otherIndex Bioactivity
dc.subject.otherIndex Cybernetics
dc.subject.otherIndex Distributed parameter networks
dc.subject.otherIndex Education
dc.subject.otherIndex Estimation
dc.subject.otherIndex Gene expression
dc.subject.otherIndex Inference engines
dc.subject.otherIndex Information theory
dc.subject.otherIndex Intelligent networks
dc.subject.otherIndex Optimal control systems
dc.subject.otherIndex Speech analysis
dc.subject.otherIndex Speech recognition
dc.subject.otherIndex Technical presentations
dc.subject.otherIndex Bayesian networks
dc.subject.otherKeywordPlus CELL-CYCLE
dc.subject.otherKeywordPlus IDENTIFICATION
dc.subject.otherKeywordPlus EXPRESSION
dc.subject.otherWOS Computer Science, Theory and Methods
dc.subject.otherWOS Engineering, Electrical and Electronic


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