Bayesian state-space modeling in gene expression data analysis: an application with biomarker prediction

DOI10.1016/j.mbs.2018.08.011zbMath1409.92084OpenAlexW2889588616WikidataQ57121737 ScholiaQ57121737MaRDI QIDQ669073

Abin Thomas, Atanu Bhattacharjee, Gajendra K. Vishwakarma

Publication date: 20 March 2019

Published in: Mathematical Biosciences (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.mbs.2018.08.011

zbMATH Keywords

Gibbs samplingMCMC algorithmgene expression predictionvariance covariance structure

Mathematics Subject Classification ID

Applications of statistics to biology and medical sciences; meta analysis (62P10) Bayesian inference (62F15) Biochemistry, molecular biology (92C40)

Related Items (6)

A weight function method for selection of proteins to predict an outcome using protein expression data ⋮ Joint modeling of longitudinal and time-to-event data on multivariate protein biomarkers ⋮ Handling missingness value on jointly measured time-course and time-to-event data ⋮ Dynamic logistic state space prediction model for clinical decision making ⋮ Time-course data prediction for repeatedly measured gene expression ⋮ A novel matched-pairs feature selection method considering with tumor purity for differential gene expression analyses

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