You might want to consider comparing your approach for variant prediction to results from following two paper: plausibly the state-of-art for variant prediction from sequence:
1. The unsupervised probabilistic modeling in of Hopf, Ingraham et al., " Mutation effects predicted from sequence co-variation" Nature Biotechnology Jan 2017https://www.nature.com/articles/nbt.3769 . Compared to 33 deep mutational scans.
2.( Unsupervised) Variational autoencoder, Riesselman, Ingraham, Marks "Deep generative models of genetic variation capture the effects of mutations" . Nature Methods 2018 https://www.nature.com/articles/s41592-018-0138-4 Compared to 40 deep mutational scans
Dear Authors
You might want to consider comparing your approach for variant prediction to results from following two paper: plausibly the state-of-art for variant prediction from sequence:
1. The unsupervised probabilistic modeling in of Hopf, Ingraham et al., " Mutation effects predicted from sequence co-variation" Nature Biotechnology Jan 2017https://www.nature.com/articles/nbt.3769 .
Compared to 33 deep mutational scans.
2.( Unsupervised) Variational autoencoder, Riesselman, Ingraham, Marks "Deep generative models of genetic variation capture the effects of mutations" . Nature Methods 2018
https://www.nature.com/articles/s41592-018-0138-4
Compared to 40 deep mutational scans
https://uploads.disquscdn.c...