Usually there are a lot of studies that have developed new methods to find candidate cancer driver genes. I have developed a new driver mutation detection algorithm. Is there any way to test my tool on some benchmarking datasets and compare it against various mutation detection algorithms already out there. I am interested only in gold standard driver mutation datasets. (Not driver gene datasets). Can you point me to any research articles or give some idea on how to test my new tool?
While I think some information has greater confidence than others, I'm not sure if "gold standard" is absolutely the best word.
My opinion is that having access to specialized knowledge is probably important. For example, for cancer, here are a couple gene-specific resources:
IARC TP53 Database: http://p53.iarc.fr/
BRCA Exchange: https://brcaexchange.org/
While not cancer related, there is also the CFTR2 reference for cystic fibrosis (which I learned from BioStars). I would tend to emphasize ClinVar (which has a star system for confidence), although that may not be perfect for all diseases. There is also the COSMIC database, but lack of being in the COSMIC database doesn't mean the variant doesn't cause cancer (and I think the number of times a variant is observed is low, even though I very much appreciate data sharing to try and maximize information available for decision making).