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About Accepted Manuscript: 10.1016/j.cmpb.2017.09.005
This paper proposes a deep learning-based ensemble method for cancer prediction that combines the outputs of five machine learning models trained on gene expression data. The method selects differentially expressed genes and uses them to train classifiers including support vector machines, random forests, neural networks, naive Bayes, and decision trees. A deep learning model then ensembles the predictions of the five classifiers. When tested on lung, stomach, and breast cancer gene expression datasets, the ensemble method achieved higher prediction accuracy than single classifiers or simple majority voting.
Detailed Information
| Author: | ['Daniel Eduardo Anasi'] |
|---|---|
| Publication Year: | 2018 |
| Pages: | 17 |
| Language: | English |
| Format: | |
| Price: | FREE |
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