Predicting loss-of-function impact of genetic mutations: a machine
  learning approach
Arshmeet Kaur Morteza Sarmadi
The innovation of next-generation sequencing (NGS) techniques has
significantly reduced the price of genome sequencing, lowering barriers to
future medical research; it is now feasible to apply genome sequencing to
studies where it would have previously been cost-inefficient. Identifying
damaging or pathogenic mutations in vast amounts of complex, high-dimensional
genome sequencing data may be of particular interest to researchers. Thus, this
paper's aims were to train machine learning models on the attributes of a
genetic mutation to predict LoFtool scores (which measure a gene's intolerance
to loss-of-function mutations). These attributes included, but were not limited
to, the position of a mutation on a chromosome, changes in amino acids, and
changes in codons caused by the mutation. Models were built using the
univariate feature selection technique f-regression combined with K-nearest
neighbors (KNN), Support Vector Machine (SVM), Random Sample Consensus
(RANSAC), Decision Trees, Random Forest, and Extreme Gradient Boosting
(XGBoost). These models were evaluated using five-fold cross-validated averages
of r-squared, mean squared error, root mean squared error, mean absolute error,
and explained variance. The findings of this study include the training of
multiple models with testing set r-squared values of 0.97.