There exists a lot of literature on distinguishing driver mutations from passengers. I am trying to build my own deep learning model to do the same. I am facing some potential issues. First, I have downloaded COSMIC mutation data and used the FATHMM labels to designate drivers (positive examples) in my dataset. I am sceptical to use passengers from COSMIC as they may be false negatives. So I turned to the 1000 genome project to download SNVs (to construct my negative examples). I am unsure if this is correct, however, I have seen some papers do the same. Do I need to apply any filters on the 1000 genome SNV data to construct the final dataset? One such paper talks of using SNVs with a global minor allele frequency≤1%.
This area of research is difficult because each person has their own opinion about what constitutes a 'driver' and 'passenger' mutation. For example, I disagree with the 1000 Genomes approach because I already know that some 1000 Genomes polymorphisms that have appreciable minor allele frequencies of around 15% in Caucasians can drive ER-positive breast cancer. To be frank: we don't have a clue what >90% of the variants listed in dbSNP / 1000 Genomes are doing. A large proportion of them could ultimately be driving cancer and other diseases.

If I were you, I would not try to define on my own what is / is not a driver and passenger. Why not utilise the work that has already been published and then follow their guidance for your deep learning model?

Look at this paper, published in the highly reputable *Cell* journal: 

 - <a
   href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6029450/">Comprehensive
   Characterization of Cancer Driver Genes and Mutations</a>.

Here is another one, published a couple of weeks ago in *Nature Genetics*:

 - <a
   href="https://www.nature.com/articles/s41588-019-0572-y">Identification
   of cancer driver genes based on nucleotide context</a>

I also just stumbled upon this online platform, which focuses on drivers:

 - <a href="https://www.intogen.org/search">intOGen</a>

Build upon the work that is already out there. Then, at least, if you try to publish your work, it will be more difficult for reviewers to criticise you.

By the way, I am not sure why you are using FATHMM, or did you mean FATHMM-MKL, as mentioned <a href="https://cancer.sanger.ac.uk/cosmic/analyses">HERE</a> on COSMIC's page. GWAVA and Funseq2 were designed for somatic mutations ( see here: https://www.biostars.org/p/286364/#286483 ).


Kevin

