Time-course data prediction for repeatedly measured gene expression

被引：6
|
作者：
Bhattacharjee, Atanu [1 ]
Vishwakarma, Gajendra K. [2 ]
机构：
[1] Tata Mem Hosp, Sect Biostat, Ctr Canc Epidemiol, Navi Mumbai 410210, India
[2] Indian Inst Technol ISM, Dept Appl Math, Dhanbad 826004, Bihar, India
来源：
INTERNATIONAL JOURNAL OF BIOMATHEMATICS | 2019年 / 12卷 / 04期
关键词：
Crossover trial; Bayesian algorithm; multiple imputation; three arms; three periods; BAYESIAN-ANALYSIS; LONGITUDINAL DATA; GROWTH; MODEL; IDENTIFICATION; DISEASE;
D O I：
10.1142/S1793524519500335
中图分类号：
Q [生物科学];
学科分类号：
07 ; 0710 ; 09 ;
摘要：
Variability in time course gene expression data is a natural phenomenon. The intention of this work is to predict the future time point data through observed sample data point. The Bayesian inference is carried to serve the objective. A total of 6 replicates 3 time point's data of 218 genes expression is adopted to illustrate the method. The estimates are found consistent with HPD interval to predict the future time point gene expression value. This proposed method can be adopted in other gene expression data setup to predict the future time course data.
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页数：16
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