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Erschienen in:
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2010 | OriginalPaper | Buchkapitel

5. Hybrid Machine Learning Model for Continuous Microarray Time Series

verfasst von : Sio-Iong Ao

Erschienen in: Advances in Machine Learning and Data Analysis

Verlag: Springer Netherlands

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Abstract

A hybrid machine learning model of the principal component analysis and neural network is described for the continuous microarray gene expression time series. The methodology can model numerically the continuous gene expression time series. The proposed model can give us the extracted features from the gene expressions time series with higher prediction accuracies. It can help practitioners to gain a better understanding of a cell cycle, and to find the dependency of genes, which is useful for drug discoveries. In this chapter, we describe the background, the machine learning algorithms, and then the application of the hybrid machine learning in the microarray analysis. The machine learning model is compared with other popular continuous prediction methods. Based on the results of two public microarray datasets, the hybrid method outperforms the other continuous prediction methods.

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Metadaten
Titel
Hybrid Machine Learning Model for Continuous Microarray Time Series
verfasst von
Sio-Iong Ao
Copyright-Jahr
2010
Verlag
Springer Netherlands
Buch
Advances in Machine Learning and Data Analysis

Print ISBN: 978-90-481-3176-1

Electronic ISBN: 978-90-481-3177-8

Copyright-Jahr: 2010

DOI
https://doi.org/10.1007/978-90-481-3177-8_5