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dc.contributor.authorBudden, David
dc.contributor.authorJones, Mitchell
dc.date.accessioned2017-10-30T14:10:58Z
dc.date.available2017-10-30T14:10:58Z
dc.date.issued2016-09
dc.identifier.issn1066-5277
dc.identifier.issn1557-8666
dc.identifier.urihttp://hdl.handle.net/1721.1/111991
dc.description.abstractModeling biology as classical problems in computer science allows researchers to leverage the wealth of theoretical advancements in this field. Despite countless studies presenting heuristics that report improvement on specific benchmarking data, there has been comparatively little focus on exploring the theoretical bounds on the performance of practical (polynomial-time) algorithms. Conversely, theoretical studies tend to overstate the generalizability of their conclusions to physical biological processes. In this article we provide a fresh perspective on the concepts of NP-hardness and inapproximability in the computational biology domain, using popular sequence assembly and alignment (mapping) algorithms as illustrative examples. These algorithms exemplify how computer science theory can both (a) lead to substantial improvement in practical performance and (b) highlight areas ripe for future innovation. Importantly, we discuss caveats that seemingly allow the performance of heuristics to exceed their provable bounds.en_US
dc.publisherMary Ann Liebert, Incen_US
dc.relation.isversionofhttp://dx.doi.org/10.1089/cmb.2016.0097en_US
dc.rightsArticle is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.en_US
dc.sourceMary Ann Lieberten_US
dc.titleCautionary Tales of Inapproximabilityen_US
dc.typeArticleen_US
dc.identifier.citationBudden, David, and Jones, Mitchell. “Cautionary Tales of Inapproximability.” Journal of Computational Biology 24, 3 (March 2017): 213–216 © 2017 Mary Ann Liebert, Incen_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.mitauthorBudden, David
dc.relation.journalJournal of Computational Biologyen_US
dc.eprint.versionFinal published versionen_US
dc.type.urihttp://purl.org/eprint/type/JournalArticleen_US
eprint.statushttp://purl.org/eprint/status/PeerRevieweden_US
dc.date.updated2017-10-30T11:37:32Z
dspace.orderedauthorsBudden, David; Jones, Mitchellen_US
dspace.embargo.termsNen_US
mit.licensePUBLISHER_POLICYen_US


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