Medulloblastoma Subgroups Prediction
Box plot
Confusion matrix
K nearest neighbor model
Linear discriminant analysis model
Model metrics
Naive bayes model
Artificial neural network model
New data prediction result
Random forest model
Input file for prediction
Input file for similarity network fusion (SNF)
Similarity network fusion (SNF)
Support vector machine model
t-SNE 3D plot
XGBoost model
Utilizing a combination of machine learning models (Random Forest, Naive Bayes, K-Nearest Neighbor, Support Vector Machines, Extreme Gradient Boosting, and Linear Discriminant Analysis) and a deep Artificial Neural Network model, 'MBMethPred' can predict medulloblastoma subgroups, including wingless (WNT), sonic hedgehog (SHH), Group 3, and Group 4 from DNA methylation beta values. See Sharif Rahmani E, Lawarde A, Lingasamy P, Moreno SV, Salumets A and Modhukur V (2023), MBMethPred: a computational framework for the accurate classification of childhood medulloblastoma subgroups using data integration and AI-based approaches. Front. Genet. 14:1233657. <doi: 10.3389/fgene.2023.1233657> for more details.
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