RandomForestModel

A function to train a random forest model to classify medulloblastoma subgroups using DNA methylation beta values (Illumina Infinium HumanMethylation450). Prediction is followed by training if new data is provided.

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.

Edris Sharif Rahmani

MBMethPred

Medulloblastoma Subgroups Prediction

Ankita Sunil Lawarde

Vijayachitra Modhukur

RandomForestModel function

<dl><dt>SplitRatio</dt>
<dd>Train and test split ratio. A value greater or equal to zero and less than one.</dd>
<dt>CV</dt>
<dd>The number of folds for cross-validation. It should be greater than one.</dd>
<dt>NTree</dt>
<dd>The number of trees to be grown.</dd>
<dt>NCores</dt>
<dd>The number of cores for parallel computing.</dd>
<dt>NewData</dt>
<dd>A methylation beta values input from the ReadMethylFile function.</dd></dl>

Arguments

Random forest model — RandomForestModel

<dl>

<dt>SplitRatio</dt>
<dd>Train and test split ratio. A value greater or equal to zero and less than one.</dd>


<dt>CV</dt>
<dd>The number of folds for cross-validation. It should be greater than one.</dd>


<dt>NTree</dt>
<dd>The number of trees to be grown.</dd>


<dt>NCores</dt>
<dd>The number of cores for parallel computing.</dd>


<dt>NewData</dt>
<dd>A methylation beta values input from the ReadMethylFile function.</dd>

</dl>

RandomForestModel: Random forest model

Description

Value

Arguments

Examples