Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/6906
Title: Integrating machine learning techniques into robust data enrichment approach and its application to gene expression data
Authors: Erdoğdu, Utku
Tan, Mehmet
Alhajj, Reda
Polat, Faruk
Rokne, Jon
Demetrick, Douglas
Keywords: gene expression data
sample generation
multiple perspectives
learning
HIMM
hierarchical markov models
genetic algorithms
PBN
probabilistic boolean networks
Issue Date: 2013
Publisher: Inderscience Enterprises Ltd
Abstract: The availability of enough samples for effective analysis and knowledge discovery has been a challenge in the research community, especially in the area of gene expression data analysis. Thus, the approaches being developed for data analysis have mostly suffered from the lack of enough data to train and test the constructed models. We argue that the process of sample generation could be successfully automated by employing some sophisticated machine learning techniques. An automated sample generation framework could successfully complement the actual sample generation from real cases. This argument is validated in this paper by describing a framework that integrates multiple models (perspectives) for sample generation. We illustrate its applicability for producing new gene expression data samples, a highly demanding area that has not received attention. The three perspectives employed in the process are based on models that are not closely related. The independence eliminates the bias of having the produced approach covering only certain characteristics of the domain and leading to samples skewed towards one direction. The first model is based on the Probabilistic Boolean Network (PBN) representation of the gene regulatory network underlying the given gene expression data. The second model integrates Hierarchical Markov Model (HIMM) and the third model employs a genetic algorithm in the process. Each model learns as much as possible characteristics of the domain being analysed and tries to incorporate the learned characteristics in generating new samples. In other words, the models base their analysis on domain knowledge implicitly present in the data itself. The developed framework has been extensively tested by checking how the new samples complement the original samples. The produced results are very promising in showing the effectiveness, usefulness and applicability of the proposed multi-model framework.
URI: https://doi.org/10.1504/IJDMB.2013.056090
https://hdl.handle.net/20.500.11851/6906
ISSN: 1748-5673
1748-5681
Appears in Collections:Bilgisayar Mühendisliği Bölümü / Department of Computer Engineering
PubMed İndeksli Yayınlar Koleksiyonu / PubMed Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection

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