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A New DP Algorithm for Comparing Gene Expression Data Using Geometric Similarity
Department of Computer Science, University of Massachusetts Boston, Boston, MA, United States.
Department of Computer Science, University of Massachusetts Boston, Boston, MA, United States.
Örebro University, School of Science and Technology. (Center for Applied Autonomous Sensor Systems (AASS))ORCID iD: 0000-0002-9607-9504
2015 (English)In: Proceedings 2015 IEEE International Conference on Bioinformatics and Biomedicine, New York: IEEE conference proceedings , 2015, 1157-1161 p.Conference paper, Published paper (Refereed)
Resource type
Text
Abstract [en]

Microarray gene expression data comes as a time series, where the expression level of a gene is recorded at specific time points. Comparing the time series produced by two genes can give us information about the regulatory or inhibitory relationship between the genes. We present a Dynamic Programming (DP) method to compare gene expression data using geometric similarity. We aim to detect similarities and relationships between genes, based on their expression time series. By representing the time series as polygons and compare them, we can find relationships that are not available when the two time series are compared point-by-point. We applied our algorithm on a dataset of 343 regulatory pairs from the alpha dataset and compared them to randomly generated pairs. Using an SVM classifier, we find the optimal similarity score that separates the regulatory dataset from the random pairs. Our results show that we can detect similar pairs better than simple Pearson correlation and we outperform many of the existing methods. This method is an ongoing approach, that can be applied to finding the similarity of any data that can convert to 2D polygon. In the future, we plan to introduce this method as a new classifier.

Place, publisher, year, edition, pages
New York: IEEE conference proceedings , 2015. 1157-1161 p.
Series
IEEE International Conference on Bioinformatics and Biomedicine-BIBM, ISSN 2156-1125
Keyword [en]
gene expression time series, clustering, Polygons, dynamic programming, polygonal chain alignment
National Category
Computer Science Biological Sciences
Research subject
Computer Science
Identifiers
URN: urn:nbn:se:oru:diva-51688DOI: 10.1109/BIBM.2015.7359846ISI: 000377335600307Scopus ID: 2-s2.0-84962385706ISBN: 978-1-4673-6798-1 (print)OAI: oai:DiVA.org:oru-51688DiVA: diva2:953268
Conference
IEEE International Conference on Bioinformatics and Biomedicine (BIBM 2015), Washington, DC, USA, November 9-12, 2015
Available from: 2016-08-17 Created: 2016-08-17 Last updated: 2017-10-18Bibliographically approved

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CiteExportLink to record
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Cite
Citation style
  • apa
  • ieee
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  • Other style
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Language
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