{"_id":"X7kyyRd8ctA2RWJKq","bibbaseid":"yogurtcu-erzin-gursoy-extractinggeneregulationinformationfrommicroarraytimeseriesdatausinghiddenmarkovmodels-2006","downloads":0,"creationDate":"2015-12-09T21:23:15.005Z","title":"Extracting gene regulation information from microarray time-series data using hidden Markov models","author_short":["Yogurtcu, O. N.","Erzin, E.","Gursoy, A."],"year":2006,"bibtype":"inproceedings","biburl":"https://drive.google.com/uc?export=download&id=1d5Wvl98W_buJq6prXBz16a_6uF9Cdt-7","bibdata":{"bibtype":"inproceedings","type":"inproceedings","author":[{"propositions":[],"lastnames":["Yogurtcu"],"firstnames":["Osman","N."],"suffixes":[]},{"propositions":[],"lastnames":["Erzin"],"firstnames":["Engin"],"suffixes":[]},{"propositions":[],"lastnames":["Gursoy"],"firstnames":["Attila"],"suffixes":[]}],"editor":[{"firstnames":[],"propositions":[],"lastnames":["Levi, A"],"suffixes":[]},{"propositions":[],"lastnames":["Savas"],"firstnames":["E"],"suffixes":[]},{"propositions":[],"lastnames":["Yenigun"],"firstnames":["H"],"suffixes":[]},{"propositions":[],"lastnames":["Balcisory"],"firstnames":["S"],"suffixes":[]},{"propositions":[],"lastnames":["Saygin"],"firstnames":["Y"],"suffixes":[]}],"title":"Extracting gene regulation information from microarray time-series data using hidden Markov models","booktitle":"Computer and Information Sciences - ISCIS 2006, Proceedings","series":"LECTURE NOTES IN COMPUTER SCIENCE","year":"2006","volume":"4263","pages":"144-153","note":"21st International Symposium on Computer and Information Sciences (ISCIS 2006), Istanbul, TURKEY, NOV 01-03, 2006","organization":"Sabanci Univ, Fac Engn & Nat Sci; Sci & Technol Res Council Turkey; Sabanci Univ; Inst Elect & Elect Engineers, Turkey Sect; IFIP","abstract":"Finding gene regulation information from microarray time-series data is important to uncover transcriptional regulatory networks. Pearson correlation is the widely used method to find similarity between time-series data. However, correlation approach fails to identify gene regulations if time-series expressions do not have global similarity, which is mostly the case. Assuming that gene regulation time-series data exhibits temporal patterns other than global similarities, one can model these temporal patterns. Hidden Markov models (HMMs) are well established structures to learn and model temporal patterns. In this study, we propose a new method to identify regulation relationships from microarray time-series data using HMMs. We showed that the proposed HMM based approach detects gene regulations, which are not captured by correlation methods. We also compared our method with recently proposed gene regulation detection approaches including edge detection, event method and dominant spectral component analysis. Results on Spellman's alpha-synchronized yeast cell-cycle data clearly present that HMM approach is superior to previous methods.","issn":"0302-9743","isbn":"3-540-47242-8","researcherid-numbers":"Erzin, Engin/H-1716-2011 Gursoy, Attila/E-9565-2015","orcid-numbers":"Erzin, Engin/0000-0002-2715-2368 Gursoy, Attila/0000-0002-2297-2113","unique-id":"ISI:000243130100017","bibtex":"@inproceedings{ ISI:000243130100017,\nAuthor = {Yogurtcu, Osman N. and Erzin, Engin and Gursoy, Attila},\nEditor = {{Levi, A and Savas, E and Yenigun, H and Balcisory, S and Saygin, Y}},\nTitle = {{Extracting gene regulation information from microarray time-series data\n using hidden Markov models}},\nBooktitle = {{Computer and Information Sciences - ISCIS 2006, Proceedings}},\nSeries = {{LECTURE NOTES IN COMPUTER SCIENCE}},\nYear = {{2006}},\nVolume = {{4263}},\nPages = {{144-153}},\nNote = {{21st International Symposium on Computer and Information Sciences (ISCIS\n 2006), Istanbul, TURKEY, NOV 01-03, 2006}},\nOrganization = {{Sabanci Univ, Fac Engn \\& Nat Sci; Sci \\& Technol Res Council Turkey;\n Sabanci Univ; Inst Elect \\& Elect Engineers, Turkey Sect; IFIP}},\nAbstract = {{Finding gene regulation information from microarray time-series data is\n important to uncover transcriptional regulatory networks. Pearson\n correlation is the widely used method to find similarity between\n time-series data. However, correlation approach fails to identify gene\n regulations if time-series expressions do not have global similarity,\n which is mostly the case. Assuming that gene regulation time-series data\n exhibits temporal patterns other than global similarities, one can model\n these temporal patterns. Hidden Markov models (HMMs) are well\n established structures to learn and model temporal patterns. In this\n study, we propose a new method to identify regulation relationships from\n microarray time-series data using HMMs.\n We showed that the proposed HMM based approach detects gene regulations,\n which are not captured by correlation methods. We also compared our\n method with recently proposed gene regulation detection approaches\n including edge detection, event method and dominant spectral component\n analysis. Results on Spellman's alpha-synchronized yeast cell-cycle data\n clearly present that HMM approach is superior to previous methods.}},\nISSN = {{0302-9743}},\nISBN = {{3-540-47242-8}},\nResearcherID-Numbers = {{Erzin, Engin/H-1716-2011\n Gursoy, Attila/E-9565-2015}},\nORCID-Numbers = {{Erzin, Engin/0000-0002-2715-2368\n Gursoy, Attila/0000-0002-2297-2113}},\nUnique-ID = {{ISI:000243130100017}},\n}\n\n","author_short":["Yogurtcu, O. N.","Erzin, E.","Gursoy, A."],"editor_short":["Levi, A","Savas, E","Yenigun, H","Balcisory, S","Saygin, Y"],"key":"ISI:000243130100017","id":"ISI:000243130100017","bibbaseid":"yogurtcu-erzin-gursoy-extractinggeneregulationinformationfrommicroarraytimeseriesdatausinghiddenmarkovmodels-2006","role":"author","urls":{},"metadata":{"authorlinks":{"erzin, e":"http://home.ku.edu.tr/~eerzin/pubs/index6.html"}}},"search_terms":["extracting","gene","regulation","information","microarray","time","series","data","using","hidden","markov","models","yogurtcu","erzin","gursoy"],"keywords":[],"authorIDs":["56689bc2b3110c264a000354","566927fe71adeb5a05000063","s4rze5RZET4EY5wXY"],"dataSources":["P7SB4qiBxZPhjXYRW","ziFHh7RJJaJNc9iie","eoMYcQtZLjtLCGT3K"]}