Analysis of mesenchymal stem cell differentiation in vitro using classification association rule mining.
In this paper, data mining is used to analyze the data on the differentiation of mammalian Mesenchymal Stem Cells (MSCs), aiming at discovering known and hidden rules governing MSC differentiation, following the establishment of a web-based public database containing experimental data on the MSC proliferation and differentiation. To this effect, a web-based public interactive database comprising the key parameters which influence the fate and destiny of mammalian MSCs has been constructed and analyzed using Classification Association Rule Mining (CARM) as a data-mining technique. The results show that the proposed approach is technically feasible and performs well with respect to the accuracy of (classification) prediction. Key rules mined from the constructed MSC database are consistent with experimental observations, indicating the validity of the method developed and the first step in the application of data mining to the study of MSCs.
Wang
Weiqi
W
Department of Engineering Science, University of Oxford, United Kingdom. weiqi.wang@eng.ox.ac.uk
Wang
Yanbo Justin
YJ
Bañares-Alcántara
René
R
Coenen
Frans
F
Cui
Zhanfeng
Z
D000465:Algorithms; D000818:Animals; D001185:Artificial Intelligence; D002454:Cell Differentiation / Q000502:physiology*; D003198:Computer Simulation; D016208:Databases, Factual; D006801:Humans; D016247:Information Storage and Retrieval; D059630:Mesenchymal Stem Cells / Q000166:cytology* / Q000502:physiology*; D008954:Models, Biological; D010363:Pattern Recognition, Automated / Q000379:methods*