Development of a Machine Learning Classifier for Brain Tumors Diagnosis Based on DNA Methylation Profile
Background: More than 150 types of brain tumors have been documented. Accurate diagnosis is important for making appropriate therapeutic decisions in treating the diseases. The goal of this study is to develop a DNA methylation profile-based classifier to accurately identify various kinds of brain t...
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| Autores principales: | , , , , , , , |
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| Formato: | article |
| Lenguaje: | EN |
| Publicado: |
Frontiers Media S.A.
2021
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| Materias: | |
| Acceso en línea: | https://doaj.org/article/0d848c47df394e3783d6250f30cce2ca |
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| Sumario: | Background: More than 150 types of brain tumors have been documented. Accurate diagnosis is important for making appropriate therapeutic decisions in treating the diseases. The goal of this study is to develop a DNA methylation profile-based classifier to accurately identify various kinds of brain tumors.Methods: Thirteen datasets of DNA methylation profiles were downloaded from the Gene Expression Omnibus (GEO) database, of which GSE90496 and GSE109379 were used as the training set and the validation set, respectively, and the remaining 11 sets were used as the independent test set. The random forest algorithm was used to select the CpG sites based on the importance of the features and a multilayer perceptron (MLP) model was trained to classify the samples. Deconvolution with the debCAM package was used to explore the cellular composition difference among tumors.Results: From training datasets with 2,801 samples, 396,568 CpG sites were retained after preprocessing, of which 767 were selected as the modeling features. A three-layer MLP model was developed, which consists of 1,320 nodes in the hidden layer, to predict the histological types of brain tumors. The prediction accuracy is 99.2, 87.0, and 96.58%, respectively, on the training, validation and test sets. The results of deconvolution analysis showed that the cell proportions of different tumor subtypes were different, and it is approximately enough to distinguish different tumor entities.Conclusion: We developed a classifier that is robust for the classification of central nervous system tumors, and tried to analyze the reasons for the classification performance. |
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