Identification of Single Nucleotide Polymorphism using Support Vector Machine on Imbalanced Data
dc.contributor.author | Hasibuan, Lailan Sahrina | |
dc.contributor.author | Kusuma, Wisnu Anania | |
dc.contributor.author | Suwarno, Willy Bayuardi | |
dc.date.accessioned | 2015-07-06T06:55:19Z | |
dc.date.available | 2015-07-06T06:55:19Z | |
dc.date.issued | 2014 | |
dc.identifier.isbn | 978-979-1421-225 | |
dc.identifier.uri | http://repository.ipb.ac.id/handle/123456789/75775 | |
dc.description.abstract | Abstract-The advance of DNA sequencing tech110/ogy presents a sig11iflca11t bioi11for111atic chal/e11ges in a do11111strea111 analysis such as identification of single nucleotide poly111orphis111 (SNP), SNP is the most ab1111da11t form of genetic 111arker and hai•e been one of the 1110s/ crucial researches in bioinfor111alics. SNP has been applied in wide area, but analysis of SNP in plants is very• limited, as in cu//ivated soybean (Glycine lltll~ l.). This paper discusse; the identijicatio11 of SNP i11 culfil'aled soybean u.-;ing Support Vector Machine (SVM). SVM is trained using positfre and negative SNP. Previous/)1, we perfor111ed a balancing positive and negative SNP with u11dersa111pling and oversa111pling to obtain training <lata. As a result, the 111otlel 1vhich is trainetl 1vitfl balancetl tlatll has better perfor111a11ce tlta11 that with i111bala11ced tlllta. | en |
dc.language.iso | en | |
dc.publisher | Universitas Indonesia | |
dc.title | Identification of Single Nucleotide Polymorphism using Support Vector Machine on Imbalanced Data | en |
dc.type | Article | en |
dc.subject.keyword | identitkalion | en |
dc.subject.keyword | SNP | en |
dc.subject.keyword | SVM | en |
dc.subject.keyword | oversampling; | en |
dc.subject.keyword | undersampling. | en |
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Proceedings [2790]
Proceedings of Bogor Agricultural University's seminars