Q guys have definitely answered why the shift is happening, like a more systems approach, things are more complex. That's kind of obvious to me. The complexity pretty much invites computer science as a, as a point of Doing things that humans cannot calculate. But what are some of the manual versus automated things that you guys described as then and now, like what's changed in the lab and people's practice?
A So a lot of my PhD work was in a field called computational biology, which is in some sense a halfway house between completely generalizable machine learning approach to answering these questions and just looking at single genes. Computational biology is really a field that's designed to use computational tools aided by the biological knowledge of the scientist that's wielding those tools. So some of the things that you can do in computational biology, for example, is take the DNA data that's coming from an Illumina sequencer, for example, tremendous amounts of data. You're going to have to go through essentially prepare that data before you can do anything with it. That step is called DNA alignment, and that used to take days. Now there are tools out there that can do the alignment process in five minutes. So that's some of the things that that field has contributed to in the last 15 years. Where I think computational biology could use help from things like machine learning is making sense of what that amount of data is actually saying about how we understand disease and how we understand human health. And that's something that manually is very hard to do because let's say you can ask what is the RNA expression level across those 20,000 genes. That doesn't answer the question of which genes are relevant For determining whether somebody has a certain disease or not. It only tel…
AI assessment note: “DNA alignment, and that used to take days. Now there are tools”