Colorectal Cancer Prediction Based on Weighted Gene Co-Expression Network Analysis and Variational Auto-Encoder

An effective feature extraction method is key to improving the accuracy of a prediction model. From the Gene Expression Omnibus (GEO) database, which includes 13,487 genes, we obtained microarray gene expression data for 238 samples from colorectal cancer (CRC) samples and normal samples. Twelve gen...

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Vydáno v:Biomolecules (Basel, Switzerland) Ročník 10; číslo 9; s. 1207
Hlavní autoři: Ai, Dongmei, Wang, Yuduo, Li, Xiaoxin, Pan, Hongfei
Médium: Journal Article
Jazyk:angličtina
Vydáno: Switzerland MDPI AG 20.08.2020
MDPI
Témata:
Accuracy
Cancer
Classification
classifier
Colorectal cancer
Colorectal carcinoma
Datasets
Discriminant analysis
DNA microarrays
Gene expression
hub genes
Methods
Neural networks
Prediction models
Principal components analysis
Researchers
Sample size
Statistical analysis
Support vector machines
variational autoencoder
weighted gene co-expression network analysis
classifier
weighted gene co-expression network analysis
colorectal cancer
hub genes
variational autoencoder
ISSN:2218-273X, 2218-273X
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Abstract An effective feature extraction method is key to improving the accuracy of a prediction model. From the Gene Expression Omnibus (GEO) database, which includes 13,487 genes, we obtained microarray gene expression data for 238 samples from colorectal cancer (CRC) samples and normal samples. Twelve gene modules were obtained by weighted gene co-expression network analysis (WGCNA) on 173 samples. By calculating the Pearson correlation coefficient (PCC) between the characteristic genes of each module and colorectal cancer, we obtained a key module that was highly correlated with CRC. We screened hub genes from the key module by considering module membership, gene significance, and intramodular connectivity. We selected 10 hub genes as a type of feature for the classifier. We used the variational autoencoder (VAE) for 1159 genes with significantly different expressions and mapped the data into a 10-dimensional representation, as another type of feature for the cancer classifier. The two types of features were applied to the support vector machines (SVM) classifier for CRC. The accuracy was 0.9692 with an AUC of 0.9981. The result shows a high accuracy of the two-step feature extraction method, which includes obtaining hub genes by WGCNA and a 10-dimensional representation by variational autoencoder (VAE).
AbstractList An effective feature extraction method is key to improving the accuracy of a prediction model. From the Gene Expression Omnibus (GEO) database, which includes 13,487 genes, we obtained microarray gene expression data for 238 samples from colorectal cancer (CRC) samples and normal samples. Twelve gene modules were obtained by weighted gene co-expression network analysis (WGCNA) on 173 samples. By calculating the Pearson correlation coefficient (PCC) between the characteristic genes of each module and colorectal cancer, we obtained a key module that was highly correlated with CRC. We screened hub genes from the key module by considering module membership, gene significance, and intramodular connectivity. We selected 10 hub genes as a type of feature for the classifier. We used the variational autoencoder (VAE) for 1159 genes with significantly different expressions and mapped the data into a 10-dimensional representation, as another type of feature for the cancer classifier. The two types of features were applied to the support vector machines (SVM) classifier for CRC. The accuracy was 0.9692 with an AUC of 0.9981. The result shows a high accuracy of the two-step feature extraction method, which includes obtaining hub genes by WGCNA and a 10-dimensional representation by variational autoencoder (VAE).
An effective feature extraction method is key to improving the accuracy of a prediction model. From the Gene Expression Omnibus (GEO) database, which includes 13,487 genes, we obtained microarray gene expression data for 238 samples from colorectal cancer (CRC) samples and normal samples. Twelve gene modules were obtained by weighted gene co-expression network analysis (WGCNA) on 173 samples. By calculating the Pearson correlation coefficient (PCC) between the characteristic genes of each module and colorectal cancer, we obtained a key module that was highly correlated with CRC. We screened hub genes from the key module by considering module membership, gene significance, and intramodular connectivity. We selected 10 hub genes as a type of feature for the classifier. We used the variational autoencoder (VAE) for 1159 genes with significantly different expressions and mapped the data into a 10-dimensional representation, as another type of feature for the cancer classifier. The two types of features were applied to the support vector machines (SVM) classifier for CRC. The accuracy was 0.9692 with an AUC of 0.9981. The result shows a high accuracy of the two-step feature extraction method, which includes obtaining hub genes by WGCNA and a 10-dimensional representation by variational autoencoder (VAE).An effective feature extraction method is key to improving the accuracy of a prediction model. From the Gene Expression Omnibus (GEO) database, which includes 13,487 genes, we obtained microarray gene expression data for 238 samples from colorectal cancer (CRC) samples and normal samples. Twelve gene modules were obtained by weighted gene co-expression network analysis (WGCNA) on 173 samples. By calculating the Pearson correlation coefficient (PCC) between the characteristic genes of each module and colorectal cancer, we obtained a key module that was highly correlated with CRC. We screened hub genes from the key module by considering module membership, gene significance, and intramodular connectivity. We selected 10 hub genes as a type of feature for the classifier. We used the variational autoencoder (VAE) for 1159 genes with significantly different expressions and mapped the data into a 10-dimensional representation, as another type of feature for the cancer classifier. The two types of features were applied to the support vector machines (SVM) classifier for CRC. The accuracy was 0.9692 with an AUC of 0.9981. The result shows a high accuracy of the two-step feature extraction method, which includes obtaining hub genes by WGCNA and a 10-dimensional representation by variational autoencoder (VAE).
Author Ai, Dongmei
Pan, Hongfei
Li, Xiaoxin
Wang, Yuduo
AuthorAffiliation 1 Basic Experimental Center of Natural Science, University of Science and Technology Beijing, Beijing 100083, China
2 School of Mathematics and Physics, University of Science and Technology Beijing, Beijing 100083, China; S20190829@xs.ustb.edu.cn (Y.W.); S20180729@xs.ustb.edu.cn (X.L.); S20170825@xs.ustb.edu.cn (H.P.)
AuthorAffiliation_xml – name: 1 Basic Experimental Center of Natural Science, University of Science and Technology Beijing, Beijing 100083, China
– name: 2 School of Mathematics and Physics, University of Science and Technology Beijing, Beijing 100083, China; S20190829@xs.ustb.edu.cn (Y.W.); S20180729@xs.ustb.edu.cn (X.L.); S20170825@xs.ustb.edu.cn (H.P.)
Author_xml – sequence: 1
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  surname: Ai
  fullname: Ai, Dongmei
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  surname: Wang
  fullname: Wang, Yuduo
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  givenname: Xiaoxin
  surname: Li
  fullname: Li, Xiaoxin
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  givenname: Hongfei
  surname: Pan
  fullname: Pan, Hongfei
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Keywords classifier
weighted gene co-expression network analysis
colorectal cancer
hub genes
variational autoencoder
Language English
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SubjectTerms Accuracy
Cancer
Classification
classifier
Colorectal cancer
Colorectal carcinoma
Datasets
Discriminant analysis
DNA microarrays
Gene expression
hub genes
Methods
Neural networks
Prediction models
Principal components analysis
Researchers
Sample size
Statistical analysis
Support vector machines
variational autoencoder
weighted gene co-expression network analysis
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Title Colorectal Cancer Prediction Based on Weighted Gene Co-Expression Network Analysis and Variational Auto-Encoder
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