Mingzhou Song, S. Boissinot, R. Haralick, I. T. Phillips
{"title":"Estimating recombination rate distribution by optimal quantization","authors":"Mingzhou Song, S. Boissinot, R. Haralick, I. T. Phillips","doi":"10.1109/CSB.2003.1227346","DOIUrl":null,"url":null,"abstract":"We obtain recombination rate distribution functions for all human chromosomes using an optimal quantization method. This nonparametric method allows us to control over-/under-fitting. The piece-wise constant recombination rate distribution functions are convenient to store and retrieve. Our experimental results showed more abrupt distribution functions than two recently published results. In the previous results, the over-/under-fitting issues were not addressed explicitly. Our estimation had greater log likelihood over a previous result using Parzen window. It suggests that the optimal quantization technique might be of great advantage for estimation of other genomic feature distributions.","PeriodicalId":147883,"journal":{"name":"Computational Systems Bioinformatics. CSB2003. Proceedings of the 2003 IEEE Bioinformatics Conference. CSB2003","volume":"44 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2003-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Computational Systems Bioinformatics. CSB2003. Proceedings of the 2003 IEEE Bioinformatics Conference. CSB2003","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CSB.2003.1227346","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2
Abstract
We obtain recombination rate distribution functions for all human chromosomes using an optimal quantization method. This nonparametric method allows us to control over-/under-fitting. The piece-wise constant recombination rate distribution functions are convenient to store and retrieve. Our experimental results showed more abrupt distribution functions than two recently published results. In the previous results, the over-/under-fitting issues were not addressed explicitly. Our estimation had greater log likelihood over a previous result using Parzen window. It suggests that the optimal quantization technique might be of great advantage for estimation of other genomic feature distributions.