基于超限学习机的快速癌症检测方法

doi:10.3969/j.issn.0253-2778.2018.02.010

中国科学技术大学学报 ›› 2018, Vol. 48 ›› Issue (2): 154-160.DOI: 10.3969/j.issn.0253-2778.2018.02.010

基于超限学习机的快速癌症检测方法

林宇鹏，谢智歌，徐凯，陈飞，刘利刚

1.中国科学技术大学数学科学学院,安徽合肥 230026；2.中国人民解放军71939部队，山东济南 250300；
3.国防科技大学计算机学院，湖南长沙 410073；4.中南大学湘雅二医院，湖南长沙 410073

收稿日期:2016-12-12 修回日期:2017-06-05 出版日期:2018-02-28 发布日期:2018-02-28
通讯作者: 刘利刚
作者简介:林宇鹏，男，1992年生，硕士.研究方向：计算机图形学，机器学习.E-mail: lypeng@mail.ustc.edu.cn
基金资助:
国家自然科学基金(61672482, 11526212, 61572507, 61540065)，中国科学院“百人计划”资助.

Fast cancer diagnosis based on extreme learning machine

LIN Yupeng, XIE Zhige, XU Kai, CHEN Fei, LIU Ligang

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）

Received:2016-12-12 Revised:2017-06-05 Online:2018-02-28 Published:2018-02-28

摘要/Abstract

摘要： 利用基于局部感受野的超限学习机(ELM-LRF)算法从给定的基因表达数据中提取有效的特征来进行癌症检测与分类.首先使用主成分分析（PCA）方法对原数据进行适当预处理，减少数据中存在的冗余，然后构建特定的特征映射，将得到的数据映射到相应特征空间中去，最后对得到的数据特征进行训练学习，得到最终训练好的特征提取模型. 实验表明，ELM-LRF的学习效率更高，取得的癌症检测效果比以往方法更好.

关键词: 超限学习机, 特征学习, 机器学习, 分类, 癌症检测

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.

Key words: extreme learning machine (ELM), feature learning, machine learning, classification, cancer diagnosis

中图分类号:

TP391

林宇鹏，谢智歌，徐凯，陈飞，刘利刚. 基于超限学习机的快速癌症检测方法[J]. 中国科学技术大学学报, 2018, 48(2): 154-160.

LIN Yupeng, XIE Zhige, XU Kai, CHEN Fei, LIU Ligang. Fast cancer diagnosis based on extreme learning machine[J]. Journal of University of Science and Technology of China, 2018, 48(2): 154-160.

参考文献

［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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基于超限学习机的快速癌症检测方法

Fast cancer diagnosis based on extreme learning machine

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