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Observer-Biased Analysis of Gene Expression Profiles
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Author(s): Paulo Fazendeiro (Instituto de Telecomunicações (IT), Portugal)and José Valente de Oliveira (University of Algarve, Portugal)
Copyright: 2015
Pages: 21
Source title:
Big Data Analytics in Bioinformatics and Healthcare
Source Author(s)/Editor(s): Baoying Wang (Waynesburg University, USA), Ruowang Li (Pennsylvania State University, USA)and William Perrizo (North Dakota State University, USA)
DOI: 10.4018/978-1-4666-6611-5.ch006
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Abstract
Microarray generated gene expression data are characterized by their volume and by the intrinsic background noise. The main task of revealing patterns in gene expression data is typically carried out using clustering analysis, with soft clustering leading the more promising candidate methods. In this chapter, Fuzzy C-Means with a variable Focal Point (FCMFP) is exploited as the first stage in gene expression data analysis. FCMFP is inspired by the observation that the visual perception of a group of similar objects is (highly) dependent on the observer position. This metaphor is used to provide a new analysis insight, with different levels of granularity, over a gene expression dataset.
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