In between refactoring some qrqc code this morning and looking at RNA-seq data, I grabbed some cold brew coffee and caught up on some missed tweets. Admittedly, my brain glosses over most tweets, but this tweet from Drew Conway had the right mix of keywords to actually make me click and read the link:

The data science debate: domain expertise or machine learning? by @medriscoll http://bit.ly/zr17Z2

I don’t mean for this title to be inflammatory, but I do believe this debate is a bit unbelievable. Machine learning is magical; I imagine that everyone that has studied it goes through a hype cycle-like set of epiphanies. This is my hype cycle story, and why I believe machine learners need to calm down, collaborate with domain experts, and together tackle hard problems.

Social Sciences and Machine Learning Caution

Biologists, it’s true: I’m not one of you. I’m a transplant from the social sciences. Specifically, from political science and economics (with statistics too), where my interests lie in methodology and comparative politics.

In the social sciences, the dimensionality of most problems is small enough that data mining is (at least in my experience) frowned upon. A lot of political data is collected by hand, often by undergraduates toiling away for meager pay as they try to understand some cryptic event coding protocol. There are some very large p data sets: The Political Instability Task Force’s data set is something I’ll keep mentioning. Mining this data with algorithms looking for interesting relationships was exactly how I was taught not to do political science.

I recall one story of a candidate giving a job talk mentioning he used forward step-wise regression to find interesting variables (in a presumably small p data set) and three people immediately stood up and left. I was proud to be knowledgeable of, but avoid statistical learning techniques. Political science had flirted with neural networks to understand massive state failure data sets, but my endless gripe was there these were predictive, not causal models. The latter required some a priori testable theory, often derived from an intimate knowledge of political crisis in a variety of countries. Just as I thought biologists knew c. elegans or s. cerevisiae well enough to form interesting experiment ideas, political scientists knew many political crises well enough to form theories and test them on a larger set of data in a quantitatively rigorous fashion. I also believed that predictive models of state failure may predict recorded (even when out-sample!) state failures well, but a model backed in a good theory that fits existing data slightly less well could predict unseen cases even better (Bruce Bueno de Mesquita has an entire wonderful book about game theory being such a model).

The Machine Learning Awakening

When I made the jump to analyzing gene expression data, I was initially astounded at how many algorithms were thrown at it. I had this vision of the hard sciences having randomization and experimentation at their disposal to lead to the purest causal findings. Looking for any differences in 30,000 genes’ expression values and then forming hypotheses after seemed backwards. Microarrays shocked biology with what they revealed about cancer and the cell, but they probably shocked the methods of experimental biology more. If your average biologist had a tenuous knowledge of p-values to begin with, now microarrays analysts were throwing around false discovery rates, empirical Bayesian techniques, Storey’s q-value, etc.

However, as I analyzed more and more sets of data, the initial reluctance I had about employing machine learning algorithms disappeared. In hype cycle terms, I was climbing the peak of inflated expectations. A quote from Michael E. Driscoll’s article captures this excitement:

Claudia Perlich, a three-time winner of the KDD Nuggets competition, stood up and shared how she had won contests in domains as varied as “breast cancer, movie prediction, and sales performance - and I can tell you I knew next to nothing about those things when I started.”

This optimism is abundant, and not entirely without justification. Coming from a non-biological background yet thoroughly understanding machine learning provides an immensely satisfying feeling of understanding of the cell. Employing all sorts of machine learning techniques, I could find “biologically interesting” genes in data sets and help biologists understand the cell.

A Few Epiphanies and a Dip of Disillusionment

The Hype Cycle’s lowest stage is the “trough of disillusionment”. Machine learning in biology certainly hasn’t had its trough (and I don’t think it will), but it is priming up to have its “slope of enlightenment” and “plateau of productivity”. There will be future machine learning hype cycles in biology, especially as multiple heterogeneous data sets need to be simultaneously mined to understand the cell with the systems approach.

My personal dip didn’t happen because machine learning left me with a particularly terrible result - it occurred because (1) because of an interaction I had with an experimental biologist and (2) I realized how wonderfully complex the cell is.

Let’s Put That in This

The first interaction I had was with a graduate student friend of mine. We were discussing an interesting finding the Korf Lab made: that some introns lead to increased expression (paper here). Introns traditionally haven’t had the same attention as promoters of enhancers in regulating gene expression. A member of the Korf lab had previously mentioned intron-mediated expression in passing to me, and I immediately started imagining what ways I could look for such an effect in silico. As I understood it, in silico was how it was first discovered, further increasing my admiration of algorithms applied to biology. When my friend mentioned it again, the first thing she said was, “well, we just need to take that intron and put it in something”.

I immediately agreed, but I realized something: I hadn’t thought of that simple step the first time I thought about intron-mediated expression. Machine learning can bring so much wealth in finding interesting relationships that my mind had glossed over the most important question in science: whether these relationships were spurious or causal. This is why my training in the social sciences was rigidly anti-machine learning: it’s far too easy to let our thought processes about understanding a complex system be biased by some spurious relationships machine learning and predictive models can quickly give us.

The Complexity of the Cell

The epiphany was gradual (and still occurring): the cell is wonderfully complex, or as my mind puts it “fucking awesomely complex”. Machine learning applied to gene expression data gives valuable insights into a complex system, but it’s really a messy snapshot. I think we’ll look at current pristine RNA-seq experiments in twenty years and we’ll realize they’re giving us an image into cellular activity that is akin to a photograph from a cheap Soviet-era camera.

Measuring gene expression from many cells glosses over interesting variation in each cell; this is certainly not a new complaint. However, even a single cell image is messy: mRNAs that make their way into gene expression values may have never been exported from the nucleus, they could have been degraded by the cell, silenced, undergone post translational modification, etc. What’s astounding is that these systems are not just complex, but are amazingly accurate. Cellular data is messy, but the cell certainly isn’t. Development is a prime example of how tightly regulated these processes are. It’s up to us to understand these tightly regulated systems with the messy images scientific data gives us. Machine learning is a necessary, but not sufficient tool to help us understand the cell.

As an example, genes interact in groups, and many algorithms can gloss over this detail. If an algorithm tries to find a sparse set of genes that are biologically interesting to the problem at hand, it may be indifferent to which it includes from a set of co-expressed genes (consider the lasso against the elastic net here). If a biologist reviews these findings, they could easily miss something vastly important based on machine learning’s indifference.

Let’s Use Both.

These epiphanies are now what guides my path through biology and machine learning. I still love and am infatuated with machine learning (although, I much prefer the phrase statistical learning). However, if we wish to understand a complex system, we need to take the approach that modern biology does: leverage machine learning with a priori biological expert knowledge to bootstrap findings. We need to design experiments that also harness the power of machine learning to help us understand, and not just predict the behavior of complex systems. Applied machine learners need to realize the power of experimental data. Chances are if you’re finding everything you think is out there with just machine learning, you’re making a mistake or your problem is too simple.