A novel algorithm for generating simulated genetic data based on K-medoids

Autor:	Chunguang Zhou, Jianan Wu, Xuefei Xia, You Zhou, Zhangxu Li, Seng Zhang
Rok vydání:	2012
Předmět:	Noise Channel (digital image) k-medoids Group method of data handling Computer science Population-based incremental learning Reliability (computer networking) Genetic algorithm Gene expression Genetic data Algorithm
Zdroj:	CCIS
DOI:	10.1109/ccis.2012.6664360
Popis:	Genetic data is very important for biological research, but it is hard to be obtained by experiment. In this paper, we introduce an algorithm for generating simulated genetic data based on K-mediods. A concept of Cluster Channel is proposed in this algorithm and used to generate simulated data. The noise of origin data could be eliminated using the proposed method. The experimental results show reliability of simulated genetic data. SAM is used to analyze the simulated data and original data, and we get a conclusion that the simulated data can effectively validate differentially expressed gene detected algorithm.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::d6b2a6b716e9c694bff9b90ba743fd07 https://doi.org/10.1109/ccis.2012.6664360 Zobrazit plný text záznamu