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      An Enhanced Grey Wolf Optimization Based Feature Selection Wrapped Kernel Extreme Learning Machine for Medical Diagnosis


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          In this study, a new predictive framework is proposed by integrating an improved grey wolf optimization (IGWO) and kernel extreme learning machine (KELM), termed as IGWO-KELM, for medical diagnosis. The proposed IGWO feature selection approach is used for the purpose of finding the optimal feature subset for medical data. In the proposed approach, genetic algorithm (GA) was firstly adopted to generate the diversified initial positions, and then grey wolf optimization (GWO) was used to update the current positions of population in the discrete searching space, thus getting the optimal feature subset for the better classification purpose based on KELM. The proposed approach is compared against the original GA and GWO on the two common disease diagnosis problems in terms of a set of performance metrics, including classification accuracy, sensitivity, specificity, precision, G-mean, F-measure, and the size of selected features. The simulation results have proven the superiority of the proposed method over the other two competitive counterparts.

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          Extreme learning machine: Theory and applications

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              Broad patterns of gene expression revealed by clustering analysis of tumor and normal colon tissues probed by oligonucleotide arrays.

              Oligonucleotide arrays can provide a broad picture of the state of the cell, by monitoring the expression level of thousands of genes at the same time. It is of interest to develop techniques for extracting useful information from the resulting data sets. Here we report the application of a two-way clustering method for analyzing a data set consisting of the expression patterns of different cell types. Gene expression in 40 tumor and 22 normal colon tissue samples was analyzed with an Affymetrix oligonucleotide array complementary to more than 6,500 human genes. An efficient two-way clustering algorithm was applied to both the genes and the tissues, revealing broad coherent patterns that suggest a high degree of organization underlying gene expression in these tissues. Coregulated families of genes clustered together, as demonstrated for the ribosomal proteins. Clustering also separated cancerous from noncancerous tissue and cell lines from in vivo tissues on the basis of subtle distributed patterns of genes even when expression of individual genes varied only slightly between the tissues. Two-way clustering thus may be of use both in classifying genes into functional groups and in classifying tissues based on gene expression.

                Author and article information

                Comput Math Methods Med
                Comput Math Methods Med
                Computational and Mathematical Methods in Medicine
                Hindawi Publishing Corporation
                26 January 2017
                : 2017
                : 9512741
                1College of Physics and Electronic Information Engineering, Wenzhou University, Wenzhou 325035, China
                2School of Digital Media, Shenzhen Institute of Information Technology, Shenzhen 518172, China
                3Cancer Hospital, Chinese Academy of Medical Sciences and Shenzhen Hospital, Shenzhen 518000, China
                Author notes

                Academic Editor: Ezequiel López-Rubio

                Copyright © 2017 Qiang Li et al.

                This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                : 8 October 2016
                : 3 December 2016
                : 21 December 2016
                Funded by: National Natural Science Foundation of China
                Award ID: 61303113
                Award ID: 61572367
                Award ID: 61571444
                Funded by: Science and Technology Plan Project of Wenzhou of China
                Award ID: G20140048
                Funded by: Zhejiang Provincial Natural Science Foundation of China
                Award ID: LY17F020012
                Award ID: LY14F020035
                Award ID: LQ13G010007
                Funded by: Guangdong Natural Science Foundation
                Award ID: 2016A030310072
                Research Article

                Applied mathematics
                Applied mathematics


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