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dc.contributor.authorKUTZNER ARNE HOLGER-
dc.date.accessioned2019-11-29T23:36:36Z-
dc.date.available2019-11-29T23:36:36Z-
dc.date.issued2017-08-
dc.identifier.citationJOURNAL OF THEORETICAL BIOLOGY, v. 427, page. 1-7en_US
dc.identifier.issn0022-5193-
dc.identifier.issn1095-8541-
dc.identifier.urihttps://www.sciencedirect.com/science/article/pii/S0022519317302163?via%3Dihub-
dc.identifier.urihttps://repository.hanyang.ac.kr/handle/20.500.11754/115313-
dc.description.abstractSimilarities among ortholog genes for a given set of species S can be expressed by alignment matrices, where each matrix cell results from aligning a gene transcript against the genome of a species within S. Gene clusters can be computed by using single-linkage clustering in time n x m, where n denotes the number of ortholog genes and m denotes the number of inspected assemblies. Our approach can break the 0(n x m) complexity of single-linkage clustering by exploiting an order among species that results from an in-order traversal of a given phylogenetic tree. The order among species allows the reduction of the inspected scope of the matrix to taxonomically related combinations of assemblies and genes, thus lowering the computational efforts necessary for creating the alignment matrix without affecting cluster quality. We present two novel approaches for clustering. First, we introduce a hierarchical clustering with, omitting the initial sorting of ISM elements, amortized O(|S|) time behavior, where it holds |S| <= n + m. Then, we propose a consecutive clustering having a linear time complexity O(|S|). Both approaches compute identical clusters, whereas dendrograms can only be obtained from the hierarchical one. We prove that our approaches deliver higher cluster densities than single linkage clustering. Additionally, we show that we compute clusters of superior quality, which ensures that our approaches are generally less error prone. (C) 2017 Elsevier Ltd. All rights reserved.en_US
dc.description.sponsorshipThis study was supported by Hanyang University. This research was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (2016R1D1A1B03932599).en_US
dc.language.isoen_USen_US
dc.publisherACADEMIC PRESS LTD- ELSEVIER SCIENCE LTDen_US
dc.subjectBURROWS-WHEELER TRANSFORMen_US
dc.subjectREAD ALIGNMENTen_US
dc.subjectSEQUENCESen_US
dc.subjectGENEen_US
dc.titleA novel specialized single-linkage clustering algorithm for taxonomically ordered dataen_US
dc.typeArticleen_US
dc.identifier.doi10.1016/j.jtbi.2017.05.008-
dc.relation.page1-7-
dc.relation.journalJOURNAL OF THEORETICAL BIOLOGY-
dc.contributor.googleauthorSchmidt, Markus-
dc.contributor.googleauthorKutzner, Arne-
dc.contributor.googleauthorHeese, Klaus-
dc.relation.code2017002898-
dc.sector.campusS-
dc.sector.daehakCOLLEGE OF ENGINEERING[S]-
dc.sector.departmentDEPARTMENT OF INFORMATION SYSTEMS-
dc.identifier.pidkutzner-
dc.identifier.orcidhttp://orcid.org/0000-0002-5601-6349-
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COLLEGE OF ENGINEERING[S](공과대학) > INFORMATION SYSTEMS(정보시스템학과) > Articles
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