  Recent advances in genomic sequencing technology have resulted in an
abundance of genome sequence data. Despite the progress in interpreting those
data, there remains a broad scope for their translation into clinical and
societal benefits. Loss-of-function variations in the human genome can be
causal in disease development. Precise identification of such variations and
pathogenicity prediction may lead to better drug targeting, among other
benefits. Machine learning comes across as a promising method for its proven
predictive ability. We have curated a novel dataset for the classification of
LOF variants using high-quality databases of genetic variation. We trained and
validated seven different classification algorithms using the new dataset to
classify the variants as Benign, Pathogenic and Likely pathogenic. We recorded
the best overall performance using the XG-Boost algorithm with an F1-score of
0.88 on the test set. We observed fair performance on Pathogenic samples with
high recall and moderate precision and subpar performance on Likely pathogenic
class, albeit with moderate precision. Overall, the encouraging results make
our final model a promising candidate for further real-world tests.
