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Network Inference in Molecular BiologyClustering Data

Network Inference in Molecular Biology: Clustering Data [Clustering reduces the size of the data by replacing individual genes with artificial super-genes that can be treated as single nodes for the purposes of network inference. By clustering genes that work together as a preprocessing step, we can improve the accuracy of the resulting network by reducing variance due to noise on individual genes. The goal is to generate clusters while losing the minimum amount of information in the dataset (and perhaps even make certain relationships stronger!). For example, if there are two genes that both behave in exactly the same way across the experimental conditions of interest, then little to no information is lost if you treat them as though they were a single “gene”.] http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png

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Publisher
Springer New York
Copyright
© The Author(s) 2012
ISBN
978-1-4614-3112-1
Pages
11 –22
DOI
10.1007/978-1-4614-3113-8_2
Publisher site
See Chapter on Publisher Site

Abstract

[Clustering reduces the size of the data by replacing individual genes with artificial super-genes that can be treated as single nodes for the purposes of network inference. By clustering genes that work together as a preprocessing step, we can improve the accuracy of the resulting network by reducing variance due to noise on individual genes. The goal is to generate clusters while losing the minimum amount of information in the dataset (and perhaps even make certain relationships stronger!). For example, if there are two genes that both behave in exactly the same way across the experimental conditions of interest, then little to no information is lost if you treat them as though they were a single “gene”.]

Published: May 24, 2012

Keywords: Simulated Annealing; Cluster Data; Association Network; Asbestos Exposure; Network Inference

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