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Mobile Classification in Microarray Experiments
Author(s) -
Dozmorov I. M.,
Centola M.,
Knowlton N.,
Tang Y.
Publication year - 2005
Publication title -
scandinavian journal of immunology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.934
H-Index - 88
eISSN - 1365-3083
pISSN - 0300-9475
DOI - 10.1111/j.1365-3083.2005.01614.x
Subject(s) - microarray analysis techniques , homogeneous , biology , organism , microarray , expression (computer science) , function (biology) , gene , variable (mathematics) , stability (learning theory) , computational biology , gene expression , evolutionary biology , genetics , mathematics , computer science , machine learning , combinatorics , programming language , mathematical analysis
In a homogeneous group of samples, there are genes whose expression variations can be attributed to factors other than experimental errors. These factors can include natural biological oscillations or metabolic processes. These genes are rarely classified as ‘interesting’ based on their variability profile. However, their dynamic behaviour can tease out important clues about naturally occurring biological processes in the organism under study and can be used for group classification. Dynamical discriminate function analysis was developed on the concept that stable classification parameters (roots) can be derived from highly variable gene‐expression data. Stability of these combinations implies a strongly compensatory relationship that may divulge functional interconnections.