This core working loop is pretty consistent, from general software (e.g., regular expressions) to “old school” scientific computing to modern AI and machine learning. What is changing today with AI/ML is the way we “abstract and generalize.” Where a mathematician might sit down with a pencil and piece of paper, or a computer scientist sits in front of a computer and thinks really hard...machine learning gives us extra leverage to use computational power and data to generate and test reasonable hypotheses. That turns out to be one of the common slow steps. So instead of sitting down and thinking really hard, we can engineer that process as well, using models that read directly from the data, find patterns, compose patterns of patterns directly from the data, and compare to what we observe. 

 

Doing it at scale on large datasets, manually/interactively ranges from tedious to impossible in a human lifetime manually. But using computation, we can automate learning loops like this to bootstrap our own (human) scientific thinking process. For example, an observation might be statistically obvious but easily overlooked by someone who is expert in a particular therapeutic area. Great, encode that pattern, test it, and ask the expert if it makes sense in this context. We can also find things that are perhaps negative examples or counterfactuals, then see how an observation doesn’t fit the pattern. These kinds of hypothesis-test loops can be especially important where testing is risky or expensive, like we often find in biology and medicine. 

 

Q: You talked a bit about domain experts having more tools to help them achieve a larger impact. How key is domain knowledge when it comes to incorporating ML and AI in a biological context?

 

It’s exactly as important as your physician going to medical school versus not!

 

Why do you trust your physician when she recommends you to have a blood test? Or when contextualizing your blood test results, if she says: “Nothing to be worried about here, this is typical for you,” what makes that judgment trustworthy? Or when you get a scary diagnosis, where do you go looking for information and how do you assimilate what you find? You can keep pulling this thread all the way back to the beginning and it comes down to a scientist observing something in a dataset and thinking,”Huh, that’s unexpected, I need to understand how this observation happened in this context,” and that leads to more experiments to gather more data about the context around that experiment and the interpretation around it. So, having an understanding of “what is known” and being able to contextualize, interpret, and explain observations is domain expertise. 

 

Experts are also able to project their observations into the space of what is known and make assertions to guide uncertain investigations; they can assimilate noisy information, and generalize their contextual observations out of that knowledge and make predictions about novel behaviors or observations in that domain: “In the context of X, if Y is true, then we should expect to observe...” When you know what is known and you see something else, you should be able to say, “Oh that fits the pattern of what is known, this makes sense to me; therefore, I’m going to predict the known outcome,” or, “Hey, I have this observation, that’s weird, that doesn’t exist in what is known, I need to understand the context better and test my assumptions.”

 

Maybe some parts are known, so you think through the analysis in parts, “I’m less confident about this, but I have a reasonable hypothesis about how this part happens in a different context, so let’s start with that observation and project it into this new context, and check our understanding with the following test.”Or it might be something that’s completely unknown. When that happens, a domain expert needs to have the confidence and scientific awareness to be able to say, “I know what is known and this isn’t in there.” One can make assertions about what is unknown when they understand the known space. But of course real-world problems are not so clean-cut. So many of these complex data analysis problems start with developing a properly calibrated estimate for what is known, and then we hone in on the answer by methodically testing away the degrees of uncertainty.

 

Domain experts can have a big impact on life sciences today because the data and contexts are so complex. So there is a big gap – domain experts can contextualize, create, and interpret data that a non-expert cannot. In general, creating and testing against high-quality datasets is a really important part of executing an automation hypothesis. And specifically in the context of life sciences where there is a flood of complex, high-dimensional data from multi-omics, sensors, electronic records, and more, you still have to contextualize and interpret these results and build understanding of what is known versus what is unknown. So in life sciences there is really no question that domain expertise is required! 

 

The more timely question to be asking is: “How do we amplify the power of a domain expert?” And our answer is to help them focus on the observations they can interpret but a non-expert cannot.