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CN 34-1054/N

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Fast cancer diagnosis based on extreme learning machine

  • 1.

    School of Mathematical Sciences, University of Science and Technology of China,Hefei 230026, China

  • 2.

    PLA 71939 Unit, Jinan 250300, China

  • 3.

    School of Computer, National University of Defense Technology, Changsha 410073, China

  • 4.

    The Second Xiangya Hospital, Central South University, Changsha 410073, China

Cite this:
https://doi.org/10.3969/j.issn.0253-2778.2018.02.010
  • Received Date: 12 December 2016
  • Rev Recd Date: 05 June 2017
  • Publish Date: 28 February 2018
    Abstract

  • Abstract

    The local receptive fields based extreme learning machine (ELM-LRF) method was utilized to learn the effective features from the acquired gene expression data to help enhance cancer diagnosis and classification. Firstly, the principal component analysis (PCA) method was implemented to process the dataset. Secondly, the features mapping to map our dataset were constructed to the specific feature space. Finally, the features to train the learning model were used to get the final ELM feature extraction model. The experiment shows that the proposed algorithm outperforms almost all the existing methods in accuracy and efficiency.

    Abstract

    The local receptive fields based extreme learning machine (ELM-LRF) method was utilized to learn the effective features from the acquired gene expression data to help enhance cancer diagnosis and classification. Firstly, the principal component analysis (PCA) method was implemented to process the dataset. Secondly, the features mapping to map our dataset were constructed to the specific feature space. Finally, the features to train the learning model were used to get the final ELM feature extraction model. The experiment shows that the proposed algorithm outperforms almost all the existing methods in accuracy and efficiency.
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    • References(31)
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    Wl C M Y, PUI C H, DOWNING J R, et al. Treatment-specific changes in gene expression discriminate in vivo drug response in human leukemia cells[J]. Nature Genetics, 2003, 34(1): 85-90.
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    YAGI T, MORIMOTO A, EGUCHI M, et al. Identification of a gene expression signature associated with pediatric AML prognosis[J]. Blood, 2003, 102(5): 1849.
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    GASHAW I, GRMMER R, KLEIN-HITPASS L, et al. Gene signatures of testicular seminoma with emphasis on expression of ets variant gene 4[J]. Cellular & Molecular Life Sciences Cmls, 2005, 62(19-20): 2359-2368.
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    PETRICOIN E F, ARDEKANI A M, HITT B A, et al. Use of proteomic patterns in serum to identify ovarian cancer[J]. Lancet, 2002, 359(9306): 572-577.
    [28]
    POMEROY S L, TAMAYO P, GAASENBEEK M, et al. Prediction of central nervous system embryonal tumour outcome based on gene expression[J]. Nature, 2002, 415(6870): 436.
    [29]
    SINGH D, FEBBO P G, ROSS K, et al. Gene expression correlates of clinical prostate cancer behavior[J]. Cancer Cell, 2002, 1(2): 203-209.
    [30]
    VERHAAK R G, WOUTERS B J, ERPELINCK C A, et al. Prediction of molecular subtypes in acute myeloid leukemia based on gene expression profiling[J]. Haematologica, 2009, 94(1): 131-134.
    [31]
    ALON U, BARKAI N, NOTTERMAN D A, et al. Broad patterns of gene expression revealed by clustering analysis of tumor and normal colon tissues probed by oligonucleotide arrays[J]. Proceedings of the National Academy of Sciences of the United States of America, 1999, 96(12): 6745-6750.
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    [1]
    WANG Z, PALADE V. Building interpretable fuzzy models for high dimensional data analysis in cancer diagnosis[J]. BMC Genomics, 2011, 12(Suppl 2): S5.
    [2]
    CHEN H, ZHAO H, SHEN J, et al. Supervised machine learning model for high dimensional gene data in colon cancer detection[C]// 2015 IEEE International Congress on Big Data. IEEE, 2015: 134-141.
    [3]
    GOLUB T R, SLONIM D K, TAMAYO P, et al. Molecular classification of cancer: Class discovery and class prediction by gene expression monitoring[J]. Science, 1999, 286(5439): 531-537.
    [4]
    HONG J H, CHO S B. Gene boosting for cancer classification based on gene expression profiles[J]. Pattern Recognition, 2009, 42(9): 1761-1767.
    [5]
    ALON U, BARKAI N, NOTTERMAN D A, et al. Broad patterns of gene expression revealed by clustering analysis of tumor and normal colon tissues probed by oligonucleotide arrays[J]. Proceedings of the National Academy of Sciences, 1999, 96(12): 6745-6750.
    [6]
    TAN A C, GILBERT D. Ensemble machine learning on gene expression data for cancer classification[J]. Applied Bioinformatics, 2003, 2(3 Suppl): S75-83.
    [7]
    LIN W J, CHEN J J. Class-imbalanced classifiers for high-dimensional data[J]. Briefings in Bioinformatics, 2013, 14(1): 13-26.
    [8]
    SHARMA A, IMOTO S, MIYANO S. A top-r feature selection algorithm for microarray gene expression data[J]. IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB), 2012, 9(3): 754-764.
    [9]
    NANNI L, BRAHNAM S, LUMINI A. Combining multiple approaches for gene microarray classification[J]. Bioinformatics, 2012, 28(8): 1151-1157.
    [10]
    FAKOOR R, LADHAK F, NAZI A, ET AL. Using deep learning to enhance cancer diagnosis and classification[C]// Proceedings of the International Conference on Machine Learning, Atlanta, Georgia, USA, 2013. JMLR: W&CP, 2013, volume 28.
    [11]
    HUANG G B, ZHU Q Y, SIEW C K. Extreme learning machine: A new learning scheme of feedforward neural networks[C]// 2004 IEEE International Joint Conference on Neural Networks. IEEE, 2004, 2: 985-990.
    [12]
    HUANG G B, ZHU Q Y, SIEW C K. Extreme learning machine: Theory and applications[J]. Neurocomputing, 2006, 70(1): 489-501.
    [13]
    HUANG G B, CHEN L, SIEW C K. Universal approximation using incremental constructive feedforward networks with random hidden nodes[J]. IEEE Transactions on Neural Networks, 2006, 17(4): 879-892.
    [14]
    HUANG G B, BAI Z, KASUN LL C, et al. Local receptive fields based extreme learning machine[J]. IEEE Computational Intelligence Magazine, 2015, 10(2): 18-29.
    [15]
    KRIZHEVSKY A, SUTSKEVER I, HINTON G E. ImageNet classification with deep convolutional neural networks[C]// International Conference on Neural Information Processing Systems. Red Hook, NY: Curran Associates Inc, 2012:1097-1105.
    [16]
    SZEGEDY C, LIU W, JIAY, et al. Going deeper with convolutions[C]// 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2015.
    [17]
    HE K, ZHANG X, REN S, et al. Deep residual learning for image recognition[C]// 2016 IEEE Conference on Computer Vision and Pattern Recognition. IEEE, 2016:770-778.
    [18]
    REN S, HE K, GIRSHICK R, et al. Faster R-CNN: Towards real-time object detection with region proposal networks[J]. IEEE Transactions on Pattern Analysis & Machine Intelligence, 2017, 39(6):1137-1149.
    [19]
    SCHERER D, MLLER A, BEHNKE S. Evaluation of pooling operations in convolutional architectures for object recognition[C]// International Conference on Artificial Neural Networks. Berlin/ Heidelberg: Springer-Verlag, 2010:92-101.
    [20]
    MILLS K I, KOHLMANN A, WILLIAMS M, et al. Microarray classification of myelodysplastic syndrome (MDS) identifies subgroups with distinct clinical outcomes and identifies patients with high risk of AML transformation[J]. Blood, 2009(114): 1063-1072.
    [21]
    FUJIWARA T, HIRAMATSU M, ISAGAWA T, et al. ASCL1-coexpression profiling but not single gene expression profiling defines lung adenocarcinomas of neuroendocrine nature with poor prognosis[J]. Lung Cancer, 2012, 75(1): 119-125.
    [22]
    WOODWARD W A, KRISHNAMURTHY S, YAMAUCHI H, et al. Genomic and expression analysis of microdissected inflammatory breast cancer[J]. Breast Cancer Research & Treatment, 2013, 138(3): 761-772.
    [23]
    KLEIN H U, RUCKERT C, KOHLMANN A, et al. Quantitative comparison of microarray experiments with published leukemia related gene expression signatures[J]. BMC Bioinformatics, 2009, 10(1): 422.
    [24]
    Wl C M Y, PUI C H, DOWNING J R, et al. Treatment-specific changes in gene expression discriminate in vivo drug response in human leukemia cells[J]. Nature Genetics, 2003, 34(1): 85-90.
    [25]
    YAGI T, MORIMOTO A, EGUCHI M, et al. Identification of a gene expression signature associated with pediatric AML prognosis[J]. Blood, 2003, 102(5): 1849.
    [26]
    GASHAW I, GRMMER R, KLEIN-HITPASS L, et al. Gene signatures of testicular seminoma with emphasis on expression of ets variant gene 4[J]. Cellular & Molecular Life Sciences Cmls, 2005, 62(19-20): 2359-2368.
    [27]
    PETRICOIN E F, ARDEKANI A M, HITT B A, et al. Use of proteomic patterns in serum to identify ovarian cancer[J]. Lancet, 2002, 359(9306): 572-577.
    [28]
    POMEROY S L, TAMAYO P, GAASENBEEK M, et al. Prediction of central nervous system embryonal tumour outcome based on gene expression[J]. Nature, 2002, 415(6870): 436.
    [29]
    SINGH D, FEBBO P G, ROSS K, et al. Gene expression correlates of clinical prostate cancer behavior[J]. Cancer Cell, 2002, 1(2): 203-209.
    [30]
    VERHAAK R G, WOUTERS B J, ERPELINCK C A, et al. Prediction of molecular subtypes in acute myeloid leukemia based on gene expression profiling[J]. Haematologica, 2009, 94(1): 131-134.
    [31]
    ALON U, BARKAI N, NOTTERMAN D A, et al. Broad patterns of gene expression revealed by clustering analysis of tumor and normal colon tissues probed by oligonucleotide arrays[J]. Proceedings of the National Academy of Sciences of the United States of America, 1999, 96(12): 6745-6750.
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