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A personalized microRNA microarray normalization method using a logistic regression model
Author(s) -
Bin Wang,
Xiaofeng Wang,
Paul Howell,
Xuemin Qian,
Kun Huang,
Adam I. Riker,
Jingfang Ju,
Yaguang Xi
Publication year - 2009
Publication title -
bioinformatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.599
H-Index - 390
eISSN - 1367-4811
pISSN - 1367-4803
DOI - 10.1093/bioinformatics/btp655
Subject(s) - normalization (sociology) , logistic regression , computer science , microarray analysis techniques , data mining , artificial intelligence , computational biology , statistics , machine learning , mathematics , biology , genetics , gene , gene expression , sociology , anthropology
MicroRNA (miRNA) is a set of newly discovered non-coding small RNA molecules. Its significant effects have contributed to a number of critical biological events including cell proliferation, apoptosis development, as well as tumorigenesis. High-dimensional genomic discovery platforms (e.g. microarray) have been employed to evaluate the important roles of miRNAs by analyzing their expression profiling. However, because of the small total number of miRNAs and the absence of well-known endogenous controls, the traditional normalization methods for messenger RNA (mRNA) profiling analysis could not offer a suitable solution for miRNA analysis. The need for the establishment of new adaptive methods has come to the forefront.

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