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dc.contributor.authorMuthukrishnan, Selvaraj
dc.contributor.authorPuri, Munish
dc.date.accessioned2018-06-26T07:36:18Z
dc.date.available2018-06-26T07:36:18Z
dc.date.issued2018-05-11
dc.identifier.citationMuthukrishnan, S., & Puri, M. (2018). Harnessing the evolutionary information on oxygen binding proteins through Support Vector Machines based modules. BMC Research Notes, 11(1). https://doi.org/10.1186/s13104-018-3383-9
dc.identifier.issn1756-0500
dc.identifier.urihttps://doi.org/10.1186/s13104-018-3383-9
dc.identifier.urihttp://hdl.handle.net/2328/38101
dc.description© The Author(s) 2018 This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
dc.description.abstractAbstract Objectives The arrival of free oxygen on the globe, aerobic life is becoming possible. However, it has become very clear that the oxygen binding proteins are widespread in the biosphere and are found in all groups of organisms, including prokaryotes, eukaryotes as well as in fungi, plants, and animals. The exponential growth and availability of fresh annotated protein sequences in the databases motivated us to develop an improved version of “Oxypred” for identifying oxygen-binding proteins. Results In this study, we have proposed a method for identifying oxy-proteins with two different sequence similarity cutoffs 50 and 90%. A different amino acid composition based Support Vector Machines models was developed, including the evolutionary profiles in the form position-specific scoring matrix (PSSM). The fivefold cross-validation techniques were applied to evaluate the prediction performance. Also, we compared with existing methods, which shows nearly 97% recognition, but, our newly developed models were able to recognize almost 99.99 and 100% in both oxy-50 and 90% similarity models respectively. Our result shows that our approaches are faster and achieve a better prediction performance over the existing methods. The web-server Oxypred2 was developed for an alternative method for identifying oxy-proteins with more additional modules including PSSM, available at http://bioinfo.imtech.res.in/servers/muthu/oxypred2/home.html .en_US
dc.language.isoenen
dc.publisherBioMed Central
dc.rights© The Author(s) 2018
dc.subjectOxygen binding proteins
dc.subjectHemoglobin
dc.subjectMyoglobin
dc.subjectLeghemoglobin
dc.subjectErythrocruorin
dc.subjectHemerythrin
dc.subjectHemocyanin
dc.subjectSupport Vector Machines
dc.subjectConfusion matrix
dc.subjectROC Analysis
dc.titleHarnessing the evolutionary information on oxygen binding proteins through Support Vector Machines based modules
dc.typeArticleen
dc.identifier.doihttps://doi.org/10.1186/s13104-018-3383-9
dc.date.updated2018-05-20T03:54:49Z
dc.rights.holderThe Author(s)
dc.rights.licenseCC-BY
local.contributor.authorOrcidLookupPuri, Munish: https://orcid.org/0000-0003-2469-3326en_US


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