Journal of Jilin University(Engineering and Technology Edition) ›› 2022, Vol. 52 ›› Issue (7): 1645-1656.doi: 10.13229/j.cnki.jdxbgxb20210127

Previous Articles    

Unsupervised feature engineering algorithm BioSAE based on sparse autoencoder

Feng-feng ZHOU1,2(),Yi-chi ZHANG1,2   

  1. 1.College of Computer Science and Technology，Jilin University，Changchun 130012，China
    2.Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education，Jilin University，Changchun 130012，China
  • Received:2021-02-14 Online:2022-07-01 Published:2022-08-08

RICH HTML

 2   

PDF (PC)

178

Abstract:

To study the internal relationship between features， a feature engineering algorithm based on sparse autoencoder （BioSAE） was proposed to encode given datasets， and it was assumed that the features encoded by sparse autoencoder might become better disease biomarkers. A comprehensive evaluation and experiment were carried out using 3494 methylation samples from 6 cancer types from TCGA. First， the encoded features were obtained through the sparse autoencoder， and then these features were analyzed and compared with the original methylation features. The experimental results show that in most modeling experiments conducted in this study， the BioSAE-encoded features are better than the original methylation features. Applying this algorithm to the datasets in the other research areas， such as image data， has also achieved a similar improvement.

Key words: computer application, feature engineering, sparse autoencoder, methylome, BioSAE

CLC Number: 

  • TP399

Table 1

Six binary classification methylation datasets of TCGA"

癌症类型样本数目总共
癌症对照
总 计30314633494
BRCA79397890
HNSC52850578
KIRC324160484
LIHC37750427
PRAD50250552
THCA50756563

Table 2

Three datasets of UCI"

数据集样本数目总 计
正样本负样本
PEMS?SF313127440
DrivFace54660606
Swarm Behaviour7 50516 51224 017

Fig.1

Schematic structure of autoencoder"

Fig.2

Experimental procedure"

Fig.3

Tuning values of two SAE parameters"

Fig.4

Comparison of differential methylation analysis between BioSAE-encoded and original features on six investigated cancer types"

Fig.5

Classification performances of six cancer types on BioSAE-encoded features"

Fig.6

Performance difference between BioSAE-encoded features and original methylation features"

Fig.7

Predictive performances of the same numbers of top-ranked BioSAE-encoded and original features"

Fig.8

Performance comparison of whether to use the feature selection algorithm"

Fig.9

Performance comparison of BioSAE-encoded features with low T-test ranks and top-ranked original features"

Fig.10

How BioSAE-encoded features improved original features on detecting early-stage thyroid carcinoma"

Fig.11

Classification performances of UCI datasets on BioSAE-encoded features and origin features"

1 Bray F, Ferlay J, Soerjomataram I, et al. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries[J]. CA: A Cancer Journal for Clinicians, 2018, 68(6): 394-424.
2 Hanahan D, Weinberg R A. Hallmarks of cancer: the next generation[J]. Cell, 2011, 144(5): 646-674.
3 Martínez-Chantar M L, Avila M A, Lu S C. Hepatocellular carcinoma: updates in pathogenesis, detection and treatment[J]. Cancers, 2020, 12(10): 1-4.
4 Kandaswamy R, Hannon E, Arseneault L, et al. DNA methylation signatures of adolescent victimization: analysis of a longitudinal monozygotic twin sample[J]. Epigenetics, 2021: 16(11):1169-1186.
5 Zafon C, Gil J, Pérez-González B, et al. DNA methylation in thyroid cancer[J]. Endocrine-Related Cancer, 2019, 26(7): 415-439.
6 Bilokapic S, Halic M. Nucleosome and ubiquitin position Set2 to methylate H3K36[J]. Nature Communications, 2019, 10: 1-9.
7 Champigny M J, Unda F, Skyba O, et al. Learning from methylomes: epigenomic correlates of Populus balsamifera traits based on deep learning models of natural DNA methylation[J]. Plant Biotechnology Journal, 2020, 18(6): 1361-1375.
8 Levy J J, Titus A J, Petersen C L, et al. MethylNet: an automated and modular deep learning approach for DNA methylation analysis[J]. Bmc Bioinformatics, 2020, 21(1): 1-15.
9 Zhang M, Pan C, Liu H, et al. An attention-based deep learning method for schizophrenia patients classification using DNA methylation data[C]∥The 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), Piscataway, USA, 2020: 172-175.
10 Shimobaba T, Endo Y, Hirayama R, et al. Autoencoder-based holographic image restoration[J]. Applied Optics, 2017, 56(13): 27-30.
11 Li F, Zurada J M, Wu W. Sparse representation learning of data by autoencoders with L1/2 regularization[J]. Neural Network, 2018, 28(2): 133-147.
12 Zhu Y, Qiu P, Ji Y. TCGA-Assembler: open-source software for retrieving and processing TCGA data[J]. Nature Methods, 2014, 11(6): 599-600.
13 Wei L, Jin Z, Yang S, et al. TCGA-assembler 2: software pipeline for retrieval and processing of TCGA/CPTAC data[J]. Bioinformatics, 2018, 34(9): 1615-1617.
14 Cuturi M. UCI Machine Learning Repository[DB/OL]. [2011-07-24]. .
15 Diaz-Chito K, Hernández-Sabaté A, López A M. A reduced feature set for driver head pose estimation[J]. Applied Soft Computing, 2016, 45: 98-107.
16 Abpeikar S, Kasmarik K, Barlow M, et al. UCI machine learning repository[DB/OL]. [2020-06-12]. .
17 Simon R. Sensitivity, specificity, PPV, and NPV for predictive biomarkers[J]. Journal of the National Cancer Institute, 2015, 107(8): 1-3.
18 Ghimatgar H, Kazemi K, Helfroush M S, et al. Neonatal EEG sleep stage classification based on deep learning and HMM[J]. Journal of Neural Engineering, 2020, 17(3): 1-17.
19 Ye Y, Zhang R, Zheng W, et al. RIFS: a randomly restarted incremental feature selection algorithm[J]. Scientific Reports, 2017, 7: 1-11.
20 李志军,杨楚皙,刘丹,等. 基于深度卷积神经网络的信息流增强图像压缩方法[J]. 吉林大学学报:工学版, 2020, 50(5): 1788-1795.
Li Zhi-jun, Yang Chu-xi, Liu Dan, et al. Deep convolutional networks based image compression with enhancement of information flow[J]. Journal of Jilin University(Engineering and Technology Edition), 2020, 50(5): 1788-1795.
21 Xu C, Liu Q, Ye M. Age invariant face recognition and retrieval by coupled auto-encoder networks[J]. Neurocomputing, 2017, 222: 62-71.
22 Yi B, Shen X, Zhang Z, et al. Expanded autoencoder recommendation framework and its application in movie recommendation[C]∥The 10th International Conference on Software, Knowledge, Information Management & Applications (SKIMA), Piscataway, USA, 2016: 298-303.
23 Sun W, Shao S, Zhao R, et al. A sparse auto-encoder-based deep neural network approach for induction motor faults classification[J]. Measurement, 2016, 89: 171-178.
24 张根保,李浩,冉琰,等. 一种用于轴承故障诊断的迁移学习模型[J]. 吉林大学学报:工学版, 2020, 50(5): 1617-1626.
Zhang Gen-bao, Li Hao, Ran Yan, et al. A transfer learning model for bearing fault diagnosis[J]. Journal of Jilin University(Engineering and Technology Edition), 2020, 50(5): 1617-1626.
25 Chen Z, Li W. Multisensor feature fusion for bearing fault diagnosis using sparse autoencoder and deep belief network[J]. IEEE Transactions on Instrumentation and Measurement, 2017, 66(7): 1693-1702.
26 Stirzaker C, Taberlay P C, Statham A L, et al. Mining cancer methylomes: prospects and challenges[J]. Trends in Genetics, 2014, 30(2): 75-84.
27 Saeed S M U, Anwar S M, Khalid H, et al. EEG based classification of long-term stress using psychological labeling[J]. Sensors, 2020, 20(7): 1-15.
28 Liao C, Li S, Luo Z. Gene selection for cancer classification using wilcoxon rank sum test and support vector machine[C]∥International Conference on Computational Intelligence and Security, Piscataway, USA, 2007: 368-373.
29 Feng G, An B, Yang F, et al. Relevance popularity: a term event model based feature selection scheme for text classification[J]. Plos One, 2017, 12(4): 1-15.
30 Cai J, Xu Y, Zhang W, et al. A comprehensive comparison of residue-level methylation levels with the regression-based gene-level methylation estimations by ReGear[J]. Briefings in Bioinformatics, 2021,22(4): 1-18.
31 Guyon I, Weston J, Barnhill S, et al. Gene selection for cancer classification using support vector machines[J]. Machine Learning, 2002, 46(1-3): 389-422.
32 Parikh S A, Gomez R, Thirugnanasambandam M, et al. Decision tree based classification of abdominal aortic aneurysms using geometry quantification measures[J]. Annals of Biomedical Engineering, 2018, 46(12): 2135-2147.
33 Hu L-Y, Huang M-W, Ke S-W, et al. The distance function effect on k-nearest neighbor classification for medical datasets[J]. Springerplus, 2016, 5(1): 1-9.
34 Zhu Y, Fang J. Logistic regression-based trichotomous classification tree and its application in medical diagnosis[J]. Medical Decision Making, 2016, 36(8): 973-989.
35 Ontivero-Ortega M, Lage-Castellanos A, Valente G, et al. Fast gaussian naïve bayes for searchlight classification analysis[J]. Neuroimage, 2017, 163: 471-479.
36 Sarica A, Cerasa A, Quattrone A. Random forest algorithm for the classification of neuroimaging data in alzheimer's disease: a systematic review[J]. Frontiers in Aging Neuroscience, 2017, 9: 1-12.
37 Huang S, Cai N, Pacheco P P, et al. Applications of support vector machine (SVM) learning in cancer genomics[J]. Cancer Genomics & Proteomics, 2018, 15(1): 41-51.
38 Hatfield G W, Hung S P, Baldi P. Differential analysis of DNA microarray gene expression data[J]. Molecular Microbiology, 2003, 47(4): 871-877.
39 King B H, Robinson C D. Differential analysis of the angle of incidence response of utility-grade PV modules[C]∥The IEEE 46th Photovoltaic Specialists Conference (Pvsc), Piscataway, USA, 2019: 77-81.
40 Liu T, Wang F, Zhu J, et al. Differential analysis on deep web data sources[C]∥IEEE International Conference on Data Mining Workshops,Piscataway, USA, 2010: 33-40.
41 Rosenberg T, Kisliouk T, Cramer T, et al. Embryonic heat conditioning induces TET-dependent cross-tolerance to hypothalamic inflammation later in life[J]. Frontiers in Genetics, 2020, 11: 1-20.
42 Wan P, Long E, Li Z, et al. TET-dependent GDF7 hypomethylation impairs aqueous humor outflow and serves as a potential therapeutic target in glaucoma[J]. Molecular Therapy, 2021, 29: 1-19.
[1] Yao-long KANG,Li-lu FENG,Jing-an ZHANG,Fu CHEN. Outlier mining algorithm for high dimensional categorical data streams based on spectral clustering [J]. Journal of Jilin University(Engineering and Technology Edition), 2022, 52(6): 1422-1427.
[2] Wen-jun WANG,Yin-feng YU. Automatic completion algorithm for missing links in nowledge graph considering data sparsity [J]. Journal of Jilin University(Engineering and Technology Edition), 2022, 52(6): 1428-1433.
[3] Xue-yun CHEN,Xue-yu BEI,Qu YAO,Xin JIN. Pedestrian segmentation and detection in multi-scene based on G-UNet [J]. Journal of Jilin University(Engineering and Technology Edition), 2022, 52(4): 925-933.
[4] Shi-min FANG. Multiple source data selective integration algorithm based on frequent pattern tree [J]. Journal of Jilin University(Engineering and Technology Edition), 2022, 52(4): 885-890.
[5] Da-xiang LI,Meng-si CHEN,Ying LIU. Spontaneous micro-expression recognition based on STA-LSTM [J]. Journal of Jilin University(Engineering and Technology Edition), 2022, 52(4): 897-909.
[6] Ming LIU,Yu-hang YANG,Song-lin ZOU,Zhi-cheng XIAO,Yong-gang ZHANG. Application of enhanced edge detection image algorithm in multi-book recognition [J]. Journal of Jilin University(Engineering and Technology Edition), 2022, 52(4): 891-896.
[7] Xiao-hui WEI,Yan-wei MIAO,Xing-wang WANG. Rhombus sketch： adaptive and more accurate sketch for streaming data [J]. Journal of Jilin University(Engineering and Technology Edition), 2022, 52(4): 874-884.
[8] Xue WANG,Zhan-shan LI,Ying-da LYU. Medical image segmentation based on multi⁃scale context⁃aware and semantic adaptor [J]. Journal of Jilin University(Engineering and Technology Edition), 2022, 52(3): 640-647.
[9] Ji-hong OUYANG,Ze-qi GUO,Si-guang LIU. Dual⁃branch hybrid attention decision net for diabetic retinopathy classification [J]. Journal of Jilin University(Engineering and Technology Edition), 2022, 52(3): 648-656.
[10] Lin MAO,Feng-zhi REN,Da-wei YANG,Ru-bo ZHANG. Two⁃way feature pyramid network for panoptic segmentation [J]. Journal of Jilin University(Engineering and Technology Edition), 2022, 52(3): 657-665.
[11] Xue-zhi WANG,Qing-liang LI,Wen-hui LI. Spatio⁃temporal model of soil moisture prediction integrated with transfer learning [J]. Journal of Jilin University(Engineering and Technology Edition), 2022, 52(3): 675-683.
[12] Su-ming KANG,Ye-e ZHANG. Hadoop⁃based local timing link prediction algorithm across social networks [J]. Journal of Jilin University(Engineering and Technology Edition), 2022, 52(3): 626-632.
[13] You QU,Wen-hui LI. Single-stage rotated object detection network based on anchor transformation [J]. Journal of Jilin University(Engineering and Technology Edition), 2022, 52(1): 162-173.
[14] Hong-wei ZHAO,Dong-sheng HUO,Jie WANG,Xiao-ning LI. Image classification of insect pests based on saliency detection [J]. Journal of Jilin University(Engineering and Technology Edition), 2021, 51(6): 2174-2181.
[15] Zhou-zhou LIU,Qian-yun ZHANG,Xin-hua MA,Han PENG. Compressed sensing signal reconstruction based on optimized discrete differential evolution algorithm [J]. Journal of Jilin University(Engineering and Technology Edition), 2021, 51(6): 2246-2252.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
No Suggested Reading articles found!
Copyright © Journal of Jilin University(Engineering and Technology Edition), All Rights Reserved.
Tel: +86-431-85095297　Fax: +86-431-85094074　E-mail: xbgxb@jlu.edu.cn
Powered by Beijing Magtech Co. Ltd