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.

本文研究了利用下一代测序技术进行基因组测序以及使用机器学习模型预测 LoFtool 分数和基因突变的相关特性，通过建立多个模型并评估其性能，得到了测试集 r 平方值为 0.97 的结果。