NDLI: Bayesian versus data driven model selection for microarray data

Content Provider	Springer Nature Link
Author	Giancarlo, Raffaele Lo Bosco, Giosué Utro, Filippo
Copyright Year	2014
Abstract	Clustering is one of the most well known activities in scientific investigation and the object of research in many disciplines, ranging from Statistics to Computer Science. In this beautiful area, one of the most difficult challenges is a particular instance of the model selection problem, i.e., the identification of the correct number of clusters in a dataset. In what follows, for ease of reference, we refer to that instance still as model selection. It is an important part of any statistical analysis. The techniques used for solving it are mainly either Bayesian or data-driven, and are both based on internal knowledge. That is, they use information obtained by processing the input data. Although both techniques have been evaluated in the realm of microarray data analysis, their merits (relative to each other) has not been assessed. Here we will fill this gap in the literature by comparing three Bayesians versus several state of the art data-driven model selection methods. Our results show that, although in some cases Bayesian methods guarantee good results, they are not able to compete in terms of ability to predict the correct number of clusters in a dataset with the data-driven methods.
Starting Page	393
Ending Page	402
Page Count	10
File Format	PDF
ISSN	15677818
Journal	Natural Computing
Volume Number	14
Issue Number	3
e-ISSN	15729796
Language	English
Publisher	Springer Netherlands
Publisher Date	2014-07-16
Publisher Place	Dordrecht
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Clustering Model selection Bayesian information criterion Akaike information criterion Minimum message length Bioinformatics Theory of Computation Evolutionary Biology Processor Architectures Artificial Intelligence (incl. Robotics) Statistical Physics, Dynamical Systems and Complexity
Content Type	Text
Resource Type	Article
Subject	Computer Science Applications

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