Since the last time I wrote a blog post, I have mainly been working on one way of verifying our algorithm; essentially, we need to make sure that our method is actually good at doing what it’s supposed to be doing. Although there are several ways to verify an algorithm, some of which Alex has been working on, I am working on something called “k-fold cross validation.”

This method of verification works by removing (or “hiding”) 1/k-th of your positives, then running your method and seeing where the hidden positives are ranked. You randomly choose the 1/k positives to hide and do this several times. You can also use the distribution of scores you get from each run to see how your method (with all positives) compares – are you getting significant results or are you getting what is expected from randomly choosing positives?

My first attempt at this was just a test run, and there were a few mistakes that I made. First, I chose to sample half instead of choosing a more reasonable number of positives such as 1/4 or 1/5. I also didn’t randomly choose positives to hide – I simply deleted the second half of the positives, and in doing so, might have removed an entire cell motility pathway or two from the positives. This would have produced biased results.

This coming week, my goal is to randomly sample 1/4 of positives multiple times, as well as implement some plotting functions in order to visualize the distribution of scores.