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dc.contributor.authorBerger Leighton, Bonnie
dc.contributor.authorDaniels, Noah
dc.contributor.authorYu, Yun William
dc.date.accessioned2018-06-19T18:04:45Z
dc.date.available2018-06-19T18:04:45Z
dc.date.issued2016-08
dc.identifier.issn0001-0782
dc.identifier.issn1557-7317
dc.identifier.urihttp://hdl.handle.net/1721.1/116419
dc.description.abstractComputational biologists answer biological and biomedical questions by using computation in support of—or in place of—laboratory procedures, hoping to obtain more accurate answers at a greatly reduced cost. The past two decades have seen unprecedented technological progress with regard to generating biological data; next-generation sequencing, mass spectrometry, microarrays, cryo-electron microscopy, and other highthroughput approaches have led to an explosion of data. However, this explosion is a mixed blessing. On the one hand, the scale and scope of data should allow new insights into genetic and infectious diseases, cancer, basic biology, and even human migration patterns. On the other hand, researchers are generating datasets so massive that it has become difficult to analyze them to discover patterns that give clues to the underlying biological processes.en_US
dc.description.sponsorshipNational Institutes of Health. (U.S.) ( grant GM108348)en_US
dc.description.sponsorshipHertz Foundationen_US
dc.publisherAssociation for Computing Machinery (ACM)en_US
dc.relation.isversionofhttp://dx.doi.org/10.1145/2957324en_US
dc.rightsCreative Commons Attribution-Noncommercial-Share Alikeen_US
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/4.0/en_US
dc.sourcePMCen_US
dc.titleComputational biology in the 21st centuryen_US
dc.typeArticleen_US
dc.identifier.citationBerger, Bonnie, Noah M. Daniels, and Y. William Yu. “Computational Biology in the 21st Century.” Communications of the ACM 59, no. 8 (July 22, 2016): 72–80, New York, NY, USA, Association for Computing Machinery (ACM), August 2016.en_US
dc.contributor.departmentMassachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratoryen_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Scienceen_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Mathematicsen_US
dc.contributor.mitauthorBerger Leighton, Bonnie
dc.contributor.mitauthorDaniels, Noah
dc.contributor.mitauthorYu, Yun William
dc.relation.journalCommunications of the ACMen_US
dc.eprint.versionAuthor's final manuscripten_US
dc.type.urihttp://purl.org/eprint/type/JournalArticleen_US
eprint.statushttp://purl.org/eprint/status/PeerRevieweden_US
dc.date.updated2018-05-16T16:36:46Z
dspace.orderedauthorsBerger, Bonnie; Daniels, Noah M.; Yu, Y. Williamen_US
dspace.embargo.termsNen_US
dc.identifier.orcidhttps://orcid.org/0000-0002-2724-7228
dc.identifier.orcidhttps://orcid.org/0000-0002-9538-825X
dc.identifier.orcidhttps://orcid.org/0000-0002-8275-9576
mit.licenseOPEN_ACCESS_POLICYen_US


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