基于改进KNN的基因序列缺失值估计研究

Cai Qing, Qingfeng Wu, Huailin Dong, Liu Han
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引用次数: 4

摘要

基因承载着生物的遗传信息,基于基因的数据挖掘受到越来越广泛的关注。基因信息挖掘的任务之一是合理有效地估计缺失值,以反映基因序列的原始信息。通过对KNN (K最近邻算法)理论的分析,提出了一种改进的基因序列KNN算法,解决了基因数据挖掘过程中缺失值的问题。通过对genbank数据的实验验证了该算法的可行性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
The research of missing value estimation of gene sequence based on improved KNN
Gene based data mining has been received wider and wider attention as gene carries genetic information of living creature. While mining gene information, one of the tasks is to estimate the missing values reasonably and effectively, so as to reflect the original information of gene sequence. By analyzing the theory of KNN (K nearest neighbor algorithm), an improved KNN for gene sequence was proposed, which resolves the problem of missing values while mining gene data. Results show the feasibility of the algorithm with experiments using data from genbank.
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