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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Veröffentlicht in:	Biomolecules (Basel, Switzerland) Jg. 10; H. 9; S. 1207
Hauptverfasser:	Ai, Dongmei, Wang, Yuduo, Li, Xiaoxin, Pan, Hongfei
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Switzerland MDPI AG 20.08.2020 MDPI
Schlagworte:	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
Online-Zugang:	Volltext
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