Hybrid method for the analysis of time series gene expression data

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

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Original languageEnglish
Pages (from-to)14-20
Journal / PublicationKnowledge-Based Systems
Publication statusPublished - Nov 2012


Time series analysis plays an increasingly important role in the study of gene expression data. Some problems, such as a large amount of noise and a small number of replicates, are computational challenges in time series expression data analysis. This paper proposes a hybrid method for analyzing time series gene expression data (HMTS). In the HMTS method, we employ a combination of K-means clustering, regression analysis and piecewise polynomial curve fitting. The K-means clustering procedure is used to divide noisy time series into different clusters, and regression analysis is used to delete outliers according to different clusters. All time series data are divided into multiple segmentations, and polynomial curve fitting is used to fit all segmentation data. The HMTS method can obtain good estimates, especially when there is noise in the data. © 2012 Elsevier B.V. All rights reserved.

Research Area(s)

  • Function approximation, Gene expression, K-means clustering, Regression analysis, Time series analysis