{"data":{"id":"10.6084/m9.figshare.c.3148168.v1","type":"dois","attributes":{"doi":"10.6084/m9.figshare.c.3148168.v1","prefix":"10.6084","suffix":"m9.figshare.c.3148168.v1","identifiers":[],"alternateIdentifiers":[],"creators":[{"name":"A. El-Lakkani","affiliation":[],"nameIdentifiers":[]},{"name":"M. Lashin","affiliation":[],"nameIdentifiers":[]}],"titles":[{"title":"An efficient method for measuring the similarity of protein sequences"}],"publisher":"Figshare","container":{},"publicationYear":2016,"subjects":[{"subject":"Biophysics"},{"subject":"Biochemistry"},{"subject":"29999 Physical Sciences not elsewhere classified","subjectScheme":"FOR"},{"subject":"FOS: Physical sciences","schemeUri":"http://www.oecd.org/science/inno/38235147.pdf","subjectScheme":"Fields of Science and Technology (FOS)"},{"subject":"FOS: Physical sciences","subjectScheme":"Fields of Science and Technology (FOS)"},{"subject":"Molecular Biology"},{"subject":"Physiology"},{"subject":"FOS: Biological sciences","schemeUri":"http://www.oecd.org/science/inno/38235147.pdf","subjectScheme":"Fields of Science and Technology (FOS)"},{"subject":"FOS: Biological sciences","subjectScheme":"Fields of Science and Technology (FOS)"},{"subject":"Evolutionary Biology"},{"subject":"69999 Biological Sciences not elsewhere classified","subjectScheme":"FOR"},{"subject":"80699 Information Systems not elsewhere classified","subjectScheme":"FOR"},{"subject":"FOS: Computer and information sciences","schemeUri":"http://www.oecd.org/science/inno/38235147.pdf","subjectScheme":"Fields of Science and Technology (FOS)"},{"subject":"FOS: Computer and information sciences","subjectScheme":"Fields of Science and Technology (FOS)"},{"subject":"Infectious Diseases"},{"subject":"FOS: Health sciences","schemeUri":"http://www.oecd.org/science/inno/38235147.pdf","subjectScheme":"Fields of Science and Technology (FOS)"},{"subject":"FOS: Health sciences","subjectScheme":"Fields of Science and Technology (FOS)"}],"contributors":[],"dates":[{"date":"2016-04-22","dateType":"Created"},{"date":"2016-05-26","dateType":"Updated"},{"date":"2016","dateType":"Issued"}],"language":null,"types":{"ris":"GEN","bibtex":"misc","citeproc":"article","schemaOrg":"Collection","resourceType":"Collection","resourceTypeGeneral":"Collection"},"relatedIdentifiers":[{"relationType":"IsSupplementTo","relatedIdentifier":"10.1080/1062936x.2016.1174735","relatedIdentifierType":"DOI"},{"relationType":"IsIdenticalTo","relatedIdentifier":"10.6084/m9.figshare.c.3148168","relatedIdentifierType":"DOI"}],"relatedItems":[],"sizes":[],"formats":[],"version":null,"rightsList":[{"rights":"CC-BY","rightsUri":"http://creativecommons.org/licenses/by/3.0/us"}],"descriptions":[{"description":"An accurate numerical descriptor for protein sequence is introduced. It is basically a set of each three successive amino acids in the sequence (triplet), starting from left to right, in addition to the distances between each two successive amino acids in the triplet such that the summation of these distances does not exceed 8. This numerical descriptor combines two features the amino acid composition and the position of each amino acid relative to the other nearby amino acids. This numerical descriptor is used to measure the similarity between protein sequences in three sets: NADH dehydrogenase subunit 5 (ND5) proteins of different species, 24 transferrin proteins from vertebrates and 12 proteins of baculoviruses. High correlation coefficient values between our results and the results of ClustalW program are obtained. These values are higher than the values obtained in many other related works.","descriptionType":"Abstract"}],"geoLocations":[],"fundingReferences":[],"xml":"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