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Ranking Function Based on Higher Order Statistics (RF-HOS) for Two-Sample Microarray Experiments

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Bioinformatics Research and Applications (ISBRA 2007)

Part of the book series: Lecture Notes in Computer Science ((LNBI,volume 4463))

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Abstract

This paper proposes a novel ranking function, called RFHOS by incorporating higher order cumulants into the ranking function for finding differentially expressed genes. Traditional ranking functions assume a data distribution (e.g., Normal) and use only first two cumulants for statistical significance analysis. Ranking functions based on second order statistics are often inadequate in ranking small sampled data (e.g., Microarray data). Also, relatively small number of samples in the data makes it hard to estimate the parameters accurately causing inaccuracies in ranking of the genes. The proposed ranking function is based on higher order statistics (RFHOS) that account for both the amplitude and the phase information by incorporating the HOS. The incorporation of HOS deviates from implicit symmetry assumed for Gaussian distribution. In this paper the performance of the RFHOS is compared against other well known ranking functions designed for ranking the genes in two sample microarray experiments.

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Authors and Affiliations

  1. Computer vision, pattern and image analysis lab(www.cvpia.org), Electrical and Computer Engineering University of Memphis Memphis TN-38152,  

    Jahangheer Shaik & Mohammed Yeasin

Authors
  1. Jahangheer Shaik
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  2. Mohammed Yeasin
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Ion Măndoiu Alexander Zelikovsky

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© 2007 Springer-Verlag Berlin Heidelberg

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Shaik, J., Yeasin, M. (2007). Ranking Function Based on Higher Order Statistics (RF-HOS) for Two-Sample Microarray Experiments. In: Măndoiu, I., Zelikovsky, A. (eds) Bioinformatics Research and Applications. ISBRA 2007. Lecture Notes in Computer Science(), vol 4463. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72031-7_9

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  • DOI: https://doi.org/10.1007/978-3-540-72031-7_9

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-72030-0

  • Online ISBN: 978-3-540-72031-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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