I'll be attending NIPS next week, and am very much looking forward to what promises to be a great scientific week.

I will also be presenting a poster on my first results in graph-based feature selection[1] at the Machine Learning in Computational Biology workshop on December 7. I've been working with Dominik Grimm, Yoshinobu Kawahara and Karsten Borgwardt on the problem of finding single-point mutations that are maximally, jointly associated with an observed trait, while being connected in an underlying (predefined) biological network. We've been rather successful at dealing with the large (10^5 to 10^7) number of features involved, as in our experiments the method turns out to be fast, robust, and generally lead to better recall than our state-of-the-art comparison partner, the overlapping group lasso, for very similar precisions.

The method is currently called SOS for Subnetworks of Optimal SNPs, but I'm not very happy with the name and I'm considering renaming it SConES (Selection of Connected Explanatory SNPs).


[1] Although I have a lot of experience treating problems in which the objects themselves are represented by graphs (and the way they are connected is very much object dependent), I had never studied a setting in which the objects are not graph-like, but there is an underlying network that connecte their features (completely independently of the objects).