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A Comprehensive Comparison of Different Clustering Methods for Reliability Analysis of Microarray Data

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      Abstract

      In this study, we considered some competitive learning methods including hard competitive learning and soft competitive learning with/without fixed network dimensionality for reliability analysis in microarrays. In order to have a more extensive view, and keeping in mind that competitive learning methods aim at error minimization or entropy maximization (different kinds of function optimization), we decided to investigate the abilities of mixture decomposition schemes. Therefore, we assert that this study covers the algorithms based on function optimization with particular insistence on different competitive learning methods. The destination is finding the most powerful method according to a pre-specified criterion determined with numerical methods and matrix similarity measures. Furthermore, we should provide an indication showing the intrinsic ability of the dataset to form clusters before we apply a clustering algorithm. Therefore, we proposed Hopkins statistic as a method for finding the intrinsic ability of a data to be clustered. The results show the remarkable ability of Rayleigh mixture model in comparison with other methods in reliability analysis task.

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      Most cited references 26

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      The self-organizing map

       T Kohonen (1990)
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        Classification, subtype discovery, and prediction of outcome in pediatric acute lymphoblastic leukemia by gene expression profiling.

        Treatment of pediatric acute lymphoblastic leukemia (ALL) is based on the concept of tailoring the intensity of therapy to a patient's risk of relapse. To determine whether gene expression profiling could enhance risk assignment, we used oligonucleotide microarrays to analyze the pattern of genes expressed in leukemic blasts from 360 pediatric ALL patients. Distinct expression profiles identified each of the prognostically important leukemia subtypes, including T-ALL, E2A-PBX1, BCR-ABL, TEL-AML1, MLL rearrangement, and hyperdiploid >50 chromosomes. In addition, another ALL subgroup was identified based on its unique expression profile. Examination of the genes comprising the expression signatures provided important insights into the biology of these leukemia subgroups. Further, within some genetic subgroups, expression profiles identified those patients that would eventually fail therapy. Thus, the single platform of expression profiling should enhance the accurate risk stratification of pediatric ALL patients.
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          Evaluating reproducibility of differential expression discoveries in microarray studies by considering correlated molecular changes

          Motivation: According to current consistency metrics such as percentage of overlapping genes (POG), lists of differentially expressed genes (DEGs) detected from different microarray studies for a complex disease are often highly inconsistent. This irreproducibility problem also exists in other high-throughput post-genomic areas such as proteomics and metabolism. A complex disease is often characterized with many coordinated molecular changes, which should be considered when evaluating the reproducibility of discovery lists from different studies. Results: We proposed metrics percentage of overlapping genes-related (POGR) and normalized POGR (nPOGR) to evaluate the consistency between two DEG lists for a complex disease, considering correlated molecular changes rather than only counting gene overlaps between the lists. Based on microarray datasets of three diseases, we showed that though the POG scores for DEG lists from different studies for each disease are extremely low, the POGR and nPOGR scores can be rather high, suggesting that the apparently inconsistent DEG lists may be highly reproducible in the sense that they are actually significantly correlated. Observing different discovery results for a disease by the POGR and nPOGR scores will obviously reduce the uncertainty of the microarray studies. The proposed metrics could also be applicable in many other high-throughput post-genomic areas. Contact: guoz@ems.hrbmu.edu.cn Supplementary information: Supplementary data are available at Bioinformatics online.
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            Author and article information

            Affiliations
            [1 ] Department of Medical Physics and Engineering, Medical School, Medical Image and Signal Processing Research Center, Isfahan University of Medical Sciences, Isfahan, Iran
            [2 ] School of Optometry and Visual Science, University of Waterloo, Waterloo, Canada
            Author notes
            Address for correspondence: Dr. Alireza Mehridehnavi, Department of Medical Physics and Engineering, Medical School, Medical Image and Signal Processing Research Center, Isfahan University of Medical Sciences, Isfahan, Iran. E-mail: mehri@ 123456med.mui.ac.ir
            Journal
            J Med Signals Sens
            JMSS
            Journal of Medical Signals and Sensors
            Medknow Publications & Media Pvt Ltd (India )
            2228-7477
            2228-7477
            Jan-Mar 2013
            : 3
            : 1
            : 22-30
            24083134
            3785067
            JMSS-3-22
            Copyright: © Journal of Medical Signals and Sensors

            This is an open-access article distributed under the terms of the Creative Commons Attribution-Noncommercial-Share Alike 3.0 Unported, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

            Categories
            Original Article

            Radiology & Imaging

            reliability analysis, microarrays, cluster validity, clustering

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