期刊
GENOMICS
卷 112, 期 3, 页码 2524-2534出版社
ACADEMIC PRESS INC ELSEVIER SCIENCE
DOI: 10.1016/j.ygeno.2020.02.004
关键词
Mouse embryonic cell; Gene expression profile; Feature selection method; Rule learning algorithm
资金
- Shanghai Municipal Science and Technology Major Project [2017SHZDZX01]
- National Key RAMP
- D Program of China [2018YFC0910403]
- National Natural Science Foundation of China [31701151]
- Natural Science Foundation of Shanghai [17ZR1412500]
- Shanghai Sailing Program [16YF1413800]
- Youth Innovation Promotion Association of Chinese Academy of Sciences (CAS) [2016245]
- Science and Technology Commission of Shanghai Municipality (STCSM) [18dz2271000]
- Key Laboratory of Stem Cell Biology of Chinese Academy of Sciences [201703]
The development of embryonic cells involves several continuous stages, and some genes are related to embryogenesis. To date, few studies have systematically investigated changes in gene expression profiles during mammalian embryogenesis. In this study, a computational analysis using machine learning algorithms was performed on the gene expression profiles of mouse embryonic cells at seven stages. First, the profiles were analyzed through a powerful Monte Carlo feature selection method for the generation of a feature list. Second, increment feature selection was applied on the list by incorporating two classification algorithms: support vector machine (SVM) and repeated incremental pruning to produce error reduction (RIPPER). Through SVM, we extracted several latent gene biomarkers, indicating the stages of embryonic cells, and constructed an optimal SVM classifier that produced a nearly perfect classification of embryonic cells. Furthermore, some interesting rules were accessed by the RIPPER algorithm, suggesting different expression patterns for different stages.
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