CNV detection method optimized for high-resolution arrayCGH by normality test
DC Field | Value | Language |
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dc.contributor.author | Ahn, Jaegyoon | - |
dc.contributor.author | Yoon, Youngmi | - |
dc.contributor.author | Park, Chihyun | - |
dc.contributor.author | Park, Sanghyun | - |
dc.date.available | 2020-02-29T06:43:20Z | - |
dc.date.created | 2020-02-05 | - |
dc.date.issued | 2012-04 | - |
dc.identifier.issn | 0010-4825 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/gachon/handle/2020.sw.gachon/16488 | - |
dc.description.abstract | High-resolution arrayCGH platform makes it possible to detect small gains and losses which previously could not be measured. However, current CNV detection tools fitted to early low-resolution data are not applicable to larger high-resolution data. When CNV detection tools are applied to high-resolution data, they suffer from high false-positives, which increases validation cost. Existing CNV detection tools also require optimal parameter values. In most cases, obtaining these values is a difficult task. This study developed a CNV detection algorithm that is optimized for high-resolution arrayCGH data. This tool operates up to 1500 times faster than existing tools on a high-resolution arrayCGH of whole human chromosomes which has 42 million probes whose average length is 50 bases, while preserving false positive/negative rates. The algorithm also uses a normality test, thereby removing the need for optimal parameters. To our knowledge, this is the first formulation for CNV detecting problems that results in a near-linear empirical overall complexity for real high-resolution data. (c) 2012 Elsevier Ltd. All rights reserved. | - |
dc.language | 영어 | - |
dc.language.iso | en | - |
dc.publisher | PERGAMON-ELSEVIER SCIENCE LTD | - |
dc.relation.isPartOf | COMPUTERS IN BIOLOGY AND MEDICINE | - |
dc.subject | COPY-NUMBER VARIATION | - |
dc.subject | CGH DATA | - |
dc.subject | HUMAN GENOME | - |
dc.subject | POLYMORPHISM | - |
dc.subject | ASSOCIATION | - |
dc.subject | NUCLEOTIDE | - |
dc.title | CNV detection method optimized for high-resolution arrayCGH by normality test | - |
dc.type | Article | - |
dc.type.rims | ART | - |
dc.description.journalClass | 1 | - |
dc.identifier.wosid | 000302524300014 | - |
dc.identifier.doi | 10.1016/j.compbiomed.2011.12.015 | - |
dc.identifier.bibliographicCitation | COMPUTERS IN BIOLOGY AND MEDICINE, v.42, no.4, pp.468 - 473 | - |
dc.identifier.scopusid | 2-s2.0-84862826319 | - |
dc.citation.endPage | 473 | - |
dc.citation.startPage | 468 | - |
dc.citation.title | COMPUTERS IN BIOLOGY AND MEDICINE | - |
dc.citation.volume | 42 | - |
dc.citation.number | 4 | - |
dc.contributor.affiliatedAuthor | Yoon, Youngmi | - |
dc.type.docType | Article | - |
dc.subject.keywordAuthor | Data mining | - |
dc.subject.keywordAuthor | Copy number variation | - |
dc.subject.keywordAuthor | High-resolution arrayCGH | - |
dc.subject.keywordAuthor | Genome analysis | - |
dc.subject.keywordAuthor | Normality test | - |
dc.subject.keywordPlus | COPY-NUMBER VARIATION | - |
dc.subject.keywordPlus | CGH DATA | - |
dc.subject.keywordPlus | HUMAN GENOME | - |
dc.subject.keywordPlus | POLYMORPHISM | - |
dc.subject.keywordPlus | ASSOCIATION | - |
dc.subject.keywordPlus | NUCLEOTIDE | - |
dc.relation.journalResearchArea | Life Sciences & Biomedicine - Other Topics | - |
dc.relation.journalResearchArea | Computer Science | - |
dc.relation.journalResearchArea | Engineering | - |
dc.relation.journalResearchArea | Mathematical & Computational Biology | - |
dc.relation.journalWebOfScienceCategory | Biology | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Interdisciplinary Applications | - |
dc.relation.journalWebOfScienceCategory | Engineering, Biomedical | - |
dc.relation.journalWebOfScienceCategory | Mathematical & Computational Biology | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
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