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Interactive visual analysis of time-series microarray data

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Abstract

Estimating dynamic regulatory pathways using DNA microarray time-series can provide invaluable information about the dynamic interactions among genes and result in new methods of rational drug design. Even though several purely computational methods have been introduced for DNA pathway analysis, most of these techniques do not provide a fully interactive method to explore and analyze these dynamic interactions in detail, which is necessary to obtain a full understanding. In this paper, we present a unified modeling and visual approach focusing on visual analysis of gene regulatory pathways over time. As a preliminary step in analyzing the gene interactions, the method applies two different techniques, a clustering algorithm and an auto regressive (AR) model. This approach provides a successful prediction of the dynamic pathways involved in the biological process under study. At this level, these pure computational techniques lack the transparency required for analysis and understanding of the gene interactions. To overcome the limitations, we have designed a visual analysis method that applies several visualization techniques, including pixel-based gene representation, animation, and multi-dimensional scaling (MDS), in a new way. This visual analysis framework allows the user to quickly and thoroughly search for and find the dynamic interactions among genes, highlight interesting gene information, show the detailed annotations of the selected genes, compare regulatory behaviors for different genes, and support gene sequence analysis for the interesting genes. In order to enhance these analysis capabilities, several methods are enabled, providing a simple graph display, a pixel-based gene visualization technique, and a relation-displaying technique among gene expressions and gene regulatory pathways.

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Authors and Affiliations

  1. Charlotte Visualization Center, The University of North Carolina at Charlotte, Charlotte, NC, USA

    Dong Hyun Jeong, Jing Yang & William Ribarsky

  2. Bioinformatics & Advanced Signal Processing Lab., Dept. of Computer Science, The University of North Carolina at Charlotte, Charlotte, NC, USA

    Alireza Darvish

  3. Biomedical Signal and Image Processing Lab., Dept. of Computer Science, School of Engineering, Virginia Commonwealth University, Richmond, VA, 23284, USA

    Kayvan Najarian

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  1. Dong Hyun Jeong
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  2. Alireza Darvish
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  3. Kayvan Najarian
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  4. Jing Yang
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  5. William Ribarsky
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Correspondence to Dong Hyun Jeong.

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Jeong, D., Darvish, A., Najarian, K. et al. Interactive visual analysis of time-series microarray data. TVC 24, 1053–1066 (2008). https://doi.org/10.1007/s00371-007-0205-9

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