Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/6601
Title: Effective Enrichment of Gene Expression Data Sets
Authors: Şirin, Utku
Erdoğdu, Utku
Tan, Mehmet
Polat, Faruk
Alhajj, Reda
Keywords: gene expression data
sample generation
multiple perspectives
learning
gene regulation modeling
probabilistic boolean networks
ordinary differential equations
Issue Date: 2012
Publisher: IEEE
Source: 11th IEEE International Conference on Machine Learning and Applications (ICMLA) -- DEC 12-15, 2012 -- Boca Raton, FL
Abstract: The ever-growing need for gene-expression data analysis motivates studies in sample generation due to the lack of enough gene-expression data. It is common that there are thousands of genes but only tens or rarely hundreds of samples available. In this paper, we attempt to formulate the sample generation task as follows: first, building alternative Gene Regulatory Network (GRN) models; second, sampling data from each of them; and then filtering the generated samples using metrics that measure compatibility, diversity and coverage with respect to the original dataset. We constructed two alternative GRN models using Probabilistic Boolean Networks and Ordinary Differential Equations. We developed a multi-objective filtering mechanism based on the three metrics to assess the quality of the newly generated data. We presented a number of experiments to show effectiveness and applicability of the proposed multi-model framework.
URI: https://doi.org/10.1109/ICMLA.2012.22
https://hdl.handle.net/20.500.11851/6601
ISBN: 978-0-7695-4913-2; 978-1-4673-4651-1
Appears in Collections:Bilgisayar Mühendisliği Bölümü / Department of Computer Engineering
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection

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