Ya making a transform to go between the physical abstractions in biology/chemistry and the data abstractions in computer science should be top priority. Frankl

Ya making a transform to go between the physical abstractions in biology/chemistry and the data abstractions in computer science should be top priority. Frankly, I don't see how biochemists today could possibly keep up with the advancements in AI and big data that have happened in just the last few years (and vice versa). I'd like to see everyone take a step back and look at treating cancer as solving a large matrix where no single cause leads to an outcome. We need genetic algorithms to quickly find local maximums and minimums, we need machine learning to see correlations between seemingly unrelated findings, we need better standards for getting longterm studies into formats that are easily grokked by AI. These are all straightforward, but I sense that there may be some resistance from the medical community in opening up the research similar to something like Folding@home. And in fairness, medical researchers are doing the heavy lifting. The first step probably has to come from hackers in the data science community, working with existing public research, and they're going to need real funding (which may not come until after the first of many cures is spotted).