Reading this quote from the original article make me nauseous. We're in a revolution with respect to cancer RESEARCH. PEOPLE are the ones being treated. You can't only focus on a math equation. You also need to focus both on palliative care and explainable research. The article seemed to save that acknowledgement for the last two sentences.
My father has been an oncology physician/researcher for 35 years. He regularly has to tell to some of his non-physician researchers that their work needs to be explainable to both his patients and those who will continue funding the research, yet they consistently show up with black box neural networks saying "we found it!"... found what exactly? He has to be able to convince his master-of-the-universe patients that he knows more than WebMD or their favorite holistic blog, see: Steve Jobs. It's incredibly more common than one would hope, see: anti-vax.
Just to clarify, there is nothing wrong with ML research into cancer. However, research isn't treatment. Oncology is depressing. Fewer and fewer doctors are interested in specializing in it. The overall suicide rate of physicians is double the general population, and that's not even oncology-specific. There's a revolution with respect to cancer treatment, and it's a pretty scary downward spiral.
Announcements such as these can end up taking money AWAY from cancer treatment. The government sees this stuff and jumps on the bandwagon. "Why fund humans when computers can do it?" Government research grants pay my father's salary along with all of his physician-researchers and students. Without that funding, people just get to read about the upcoming research while they wait to die.
Derek Lowe's take, as usual, is a good counterpoint and a giant "Take With Lump Of Salt" NB.
>I have beaten on this theme many times on the blog, so for those who haven’t heard me rant on the subject, let me refer you to this post and the links in it. Put shortly – and these sorts of stories tend to put actual oncology researchers in a pretty short mood – the cell/computer analogy is too facile to be useful. And that goes, with chocolate sprinkles on it, for all the subsidiary analogies, such as DNA/source code, disease/bug, etc. One one level, these things do sort of fit, but it’s not a level that you can get much use out of. DNA is much, much messier than any usable code ever written, and it’s messier on several different levels and in a lot of different ways. These (which include the complications of transcriptional regulation, post-transcriptional modification, epigenetic factors, repair mechanisms and mutation rates, and much, much, more), have no good analogies (especially when taken together) in coding. And these DNA-level concerns are only the beginning! That’s where you start working on an actual therapy; that’s what we call “Target ID”, and it’s way, way back in the process of finding a drug. So many complications await you after that – you can easily spend your entire working life on them, and many of us have.
And that’s why many of us who have actually been working on diseases like cancer get a little testy when we see folks from computer science coming in with this “Gosh darn it fellows, do I have to do everything myself?” attitude. Years of working with (human-designed) hardware running (human-written) code have given many people in that field what I think is an exaggerated idea of human capabilities (at least as they are right now). When you can write code that gets used by hundreds of millions of people in their daily lives, it’s understandable to think that you’re able to just reach in and change reality by sheer braininess and force of will. Unfortunately the world of code and computational hardware, as important, useful, and lucrative as it is, is just a sandbox compared to the real physical universe, of which living creatures are just a tiny little part. But biology has no debugging programs, no annotations, no manuals. It wasn’t written by humans – in fact, as far as we know, it wasn’t written by anyone at all, it “just grew” in a process that has no good counterpart to the ways that humans generally get things done. ....
>If you remove the hubris from the Microsoft announcement, though, which takes sandblasters and water cannons, you get to something that could be interesting. It’s another machine learning approach to biology, from what I can make out, and I’m not opposed in principle to that sort of thing at all. It has to be approached with caution, though, because any application of machine learning to the biology literature has to take into account that a good percentage of that literature is crap, and that negative results (which have great value for these systems) are grievously underrepresented in it as well. I think that machine approaches to understanding biological pathways will, in the end, probably be the way to go, because it’s too complex for us to keep it all straight in our minds (not human!) But we’re not there. There are many, many important things that we simply don’t understand very well, and many others, I’m sure, that we just flat-out don’t even know exist yet. Debug that.
So if Microsoft wants to apply machine learning to cancer biology, I’m all for it. But they should just go and try it and report back when something interesting comes out of it, rather than beginning by making a big noise in the newspapers. You want to cure cancer? Go do it; don’t sit around giving interviews about how you’re going to cure cancer real soon now. I’m sure that someone at the company imagines this as a big blazing publicity sendoff, fireworks and balloons and all the rest of it. But to anyone who works in the field, it’s more like a grenade going off inside a manure pile.