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The International Journal of Biostatistics

Ed. by Chambaz, Antoine / Hubbard, Alan E. / van der Laan, Mark J.

IMPACT FACTOR 2018: 1.309

CiteScore 2018: 1.11

SCImago Journal Rank (SJR) 2018: 1.325
Source Normalized Impact per Paper (SNIP) 2018: 0.715

Mathematical Citation Quotient (MCQ) 2018: 0.03

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A Pseudo-EM Algorithm for Clustering Incomplete Longitudinal Data

Mateen Shaikh / Paul D McNicholas / Anthony F Desmond
Published Online: 2010-03-15 | DOI: https://doi.org/10.2202/1557-4679.1223

A method for clustering incomplete longitudinal data, and gene expression time course data in particular, is presented. Specifically, an existing method that utilizes mixtures of multivariate Gaussian distributions with modified Cholesky-decomposed covariance structure is extended to accommodate incomplete data. Parameter estimation is carried out in a fashion that is similar to an expectation-maximization algorithm. We focus on the particular application of clustering incomplete gene expression time course data. In this application, our approach gives good clustering performance when compared to the results when there is no missing data. Possible extensions of this work are also suggested.

Keywords: clustering; gene expression time course data; longitudinal data; missing data; mixture models; pseudo-EM

About the article

Published Online: 2010-03-15

Citation Information: The International Journal of Biostatistics, Volume 6, Issue 1, ISSN (Online) 1557-4679, DOI: https://doi.org/10.2202/1557-4679.1223.

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