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

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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

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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"

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