A laminar augmented cascading flexible neural forest model for classification of cancer subtypes based on gene expression data

Background: Correctly classifying the subtypes of cancer is of great significance for the in-depth study of cancer pathogenesis and the realization of personalized treatment for cancer patients. In recent years, classification of cancer subtypes using deep neural networks and gene expression data ha...

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Bibliographic Details
Main Authors:	Chen, Y. (Author), Du, L. (Author), Meng, Q. (Author), Wu, P. (Author), Zhong, L. (Author)
Format:	Article
Language:	English
Published:	BioMed Central Ltd 2021
Subjects:	article cancer model Cancer pathogenesis Cancer patients Cancer subtype Cancer subtypes Cascade forest Classification Classification (of information) classifier deep learning Deep learning Deep neural networks Diseases Ensemble methods forest Forest modelling Forestry gene expression Gene expression Gene Expression Gene Expression Data genetics Hotspots human Humans In-depth study learning Learning neoplasm Neoplasms Patient treatment prediction RNA sequencing RNA-Seq sample size Sample Size Structural design structure activity relation
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A laminar augmented cascading flexible neural forest model for classification of cancer subtypes based on gene expression data

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