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Automated Classification of DNA Structure from Sequence Information

Descriptive

Language
LanguageTerm (authority = ISO 639-3:2007); (type = text)
English
Genre (authority = RULIB-FS)
Other
Genre (authority = marcgt)
technical report
PhysicalDescription
InternetMediaType
application/pdf
Extent
22 pages
Note (type = special display note)
Technical report DCS-TR-331
Name (type = corporate); (authority = RutgersOrg-School)
NamePart
School of Arts and Sciences (SAS) (New Brunswick)
Name (type = corporate); (authority = RutgersOrg-Department)
NamePart
Computer Science (New Brunswick)
TypeOfResource
Text
Name (type = personal)
NamePart (type = family)
Loewenstern
NamePart (type = given)
David
Affiliation
Computer Science (New Brunswick)
Name (type = personal)
NamePart (type = family)
Berman
NamePart (type = given)
Helen M.
Affiliation
Computer Science (New Brunswick)
Name (type = personal)
NamePart (type = family)
Hirsh
NamePart (type = given)
Haym
Affiliation
Computer Science (New Brunswick)
TitleInfo
Title
Automated Classification of DNA Structure from Sequence Information
Abstract (type = abstract)
We introduce an algorithm, lllama, which combines simple pattern recognizers into a general method for estimating the entropy of a sequence. Each pattern recognizer exploits a partial match between subsequences to build a model of the sequence. Since the primary features of interest in biological sequence domains are subsequences with small variations in exact composition, lllama is particularly suited to such domains. We describe two methods, lllama-length and lllama-alone, which use this entropy estimate to perform maximum a posteriori classi cation. We apply these methods to several problems in three-dimensional structure classi cation of short DNA sequences. The results include a surprisingly low 3.6% error rate in predicting helical conformation of oligonucleotides. We compare our results to those obtained using more traditional methods for automated generation of classi ers
OriginInfo
DateIssued (encoding = w3cdtf); (keyDate = yes)
1997-06
RelatedItem (type = host)
TitleInfo
Title
Computer Science (New Brunswick)
Identifier (type = local)
rucore21032500001
Location
PhysicalLocation (authority = marcorg); (displayLabel = Rutgers, The State University of New Jersey)
NjNbRU
Identifier (type = doi)
doi:10.7282/T35T3Q4J
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This Item is protected by copyright and/or related rights.You are free to use this Item in any way that is permitted by the copyright and related rights legislation that applies to your use.For other uses you need to obtain permission from the rights-holder(s).
Copyright
Status
Copyright protected
Availability
Status
Open
Reason
Permission or license
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Technical

RULTechMD (ID = TECHNICAL1)
ContentModel
Document
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1.4
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GPL Ghostscript 9.07
DateCreated (point = start); (encoding = w3cdtf); (qualifier = exact)
2018-06-06T12:27:33
DateCreated (point = start); (encoding = w3cdtf); (qualifier = exact)
2018-06-06T12:27:33
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