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In this paper, we describe an optimized approach for DNA sequence analysis on a heterogeneous platform that is accelerated with the Intel Xeon Phi. Such platforms commonly comprise one or two general purpose host central processing units (CPUs) and one or more Xeon Phi devices. We present a parallel algorithm that shares the work of DNA sequence analysis between the host CPUs and the Xeon Phi device to reduce the overall analysis time. For automatic worksharing we use a supervised machine learning approach, which predicts the performance of DNA sequence analysis on the host and device and accordingly maps fractions of the DNA sequence to the host and device. We evaluate our approach empirically using real-world DNA segments for human and various animals on a heterogeneous platform that comprises two 12-core Intel Xeon E5 CPUs and an Intel Xeon Phi 7120P device with 61 cores. <\/jats:p>","DOI":"10.1177\/1094342016654214","type":"journal-article","created":{"date-parts":[[2016,6,29]],"date-time":"2016-06-29T02:44:18Z","timestamp":1467168258000},"page":"363-379","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":13,"title":["A machine learning approach for accelerating DNA sequence analysis"],"prefix":"10.1177","volume":"32","author":[{"given":"Suejb","family":"Memeti","sequence":"first","affiliation":[{"name":"Department of Computer Science, Linnaeus University, Sweden"}]},{"given":"Sabri","family":"Pllana","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Linnaeus University, Sweden"}]}],"member":"179","published-online":{"date-parts":[[2016,6,26]]},"reference":[{"volume-title":"The 14th annual post graduate symposium on the convergence oftelecommunications, networking and broadcasting (PGNet2013)","year":"2013","author":"Bellekens X","key":"bibr1-1094342016654214"},{"key":"bibr2-1094342016654214","doi-asserted-by":"publisher","DOI":"10.1145\/2482767.2482794"},{"key":"bibr3-1094342016654214","doi-asserted-by":"publisher","DOI":"10.1016\/j.procs.2013.05.170"},{"journal-title":"Intel White Paper","year":"2014","author":"Chrysos G","key":"bibr4-1094342016654214"},{"key":"bibr5-1094342016654214","doi-asserted-by":"publisher","DOI":"10.1038\/nature01626"},{"volume-title":"Mastering Regular Expressions","year":"2002","author":"Friedl JE","key":"bibr6-1094342016654214"},{"key":"bibr7-1094342016654214","doi-asserted-by":"publisher","DOI":"10.1145\/2464576.2482738"},{"key":"bibr8-1094342016654214","doi-asserted-by":"publisher","DOI":"10.1007\/s11227-014-1266-y"},{"key":"bibr9-1094342016654214","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-19861-8_16"},{"volume-title":"Introduction to Automata Theory, Languages and Computation","year":"1979","author":"Hopcroft J","key":"bibr10-1094342016654214"},{"key":"bibr11-1094342016654214","doi-asserted-by":"publisher","DOI":"10.1109\/DATE.2012.6176582"},{"key":"bibr12-1094342016654214","doi-asserted-by":"publisher","DOI":"10.1109\/PCI.2009.47"},{"key":"bibr13-1094342016654214","doi-asserted-by":"publisher","DOI":"10.1109\/SASP.2011.5941082"},{"key":"bibr14-1094342016654214","doi-asserted-by":"publisher","DOI":"10.1109\/TC.2012.254"},{"key":"bibr15-1094342016654214","doi-asserted-by":"publisher","DOI":"10.1109\/TIFS.2011.2112647"},{"key":"bibr16-1094342016654214","first-page":"609","volume":"35","author":"Luftig MA","year":"2000","journal-title":"New England Law Review"},{"key":"bibr17-1094342016654214","doi-asserted-by":"publisher","DOI":"10.1145\/1669112.1669121"},{"key":"bibr18-1094342016654214","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0022751"},{"key":"bibr19-1094342016654214","doi-asserted-by":"publisher","DOI":"10.1109\/CSE.2014.146"},{"key":"bibr20-1094342016654214","doi-asserted-by":"publisher","DOI":"10.1109\/Trustcom.2015.636"},{"key":"bibr21-1094342016654214","unstructured":"NCBI (2015) National Center for Biotechnology Information U.S. National Library of Medicine. 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