I find myself having conversations recently with people from increasingly diverse fields, both at Columbia and in local startups, about how their work is becoming “data-informed” or “data-driven,” and about the challenges posed by applied computational statistics or big data.
A view from health and biology in the 1990s
In discussions with, as examples, New York City journalists, physicists, or even former students now working in advertising or social media analytics, I’ve been struck by how many of the technical challenges and lessons learned are reminiscent of those faced in the health and biology communities over the last 15 years, when these fields experienced their own data-driven revolutions and wrestled with many of the problems now faced by people in other fields of research or industry.
It was around then, as I was working on my PhD thesis, that sequencing technologies became sufficient to reveal the entire genomes of simple organisms and, not long thereafter, the first draft of the human genome. This advance in sequencing technologies made possible the “high throughput” quantification of, for example,
- the dynamic activity of all the genes in an organism; or
- the set of all protein-protein interactions in an organism; or even
- statistical comparative genomics revealing how small differences in genotype correlate with disease or other phenotypes.
These advances required formation of multidisciplinary collaborations, multi-departmental initiatives, advances in technologies for dealing with massive datasets, and advances in statistical and mathematical methods for making sense of copious natural data.
The fourth paradigm
This shift wasn’t just a series of technological advances in biological research; the more important change was a realization that research in which data vastly outstrip our ability to posit models is qualitatively different. Much of science for the last three centuries advanced by deriving simple models from first principles — models whose predictions could then be compared with novel experiments. In modeling complex systems for which the underlying model is not yet known but for which data are abundant, however, as in systems biology or social network analysis, one may turn this process on its head by using the data to learn not only parameters of a single model but to select which among many or an infinite number of competing models is favored by the data. Just over a half-decade ago, the computer scientist Jim Gray described this as a “fourth paradigm” of science, after experimental, theoretical, and computational paradigms. Gray predicted that every sector of human endeavor will soon emulate biology’s example of identifying data-driven research and modeling as a distinct field.
In the years since then we’ve seen just that. Examples include data-driven social sciences (often leveraging the massive data now available through social networks) and even data-driven astronomy (cf., Astrometry.net). I’ve personally enjoyed seeing many students from Columbia’s School of Engineering and Applied Science (SEAS), trained in applications of big data to biology, go on to develop and apply data-driven models in these fields. As one example, a recent SEAS PhD student spent a summer as a “hackNY Fellow” applying machine learning methods at the data-driven dating NYC startup OKCupid. [Disclosure: I’m co-founder and co-president of hackNY.] He’s now applying similar methods to population genetics as a postdoctoral researcher at the University of Chicago. These students, often with job titles like “data scientist,” are able to translate to other fields, or even to the “real world” of industry and technology-driven startups, methods needed in biology and health for making sense of abundant natural data.
Data science: Combining engineering and natural sciences
In my research group, our work balances “engineering” goals, e.g., developing models that can make accurate quantitative predictions, with “natural science” goals, meaning building models that are interpretable to our biology and clinical collaborators, and which suggest to them what novel experiments are most likely to reveal the workings of natural systems. For example:
- We’ve developed machine-learning methods for modeling the expression of genes — the “on-off” state of the tens of thousands of individual processes your cells execute — by combining sequence data with microarray expression data. These models reveal which genes control which other genes, via what important sequence elements.
- We’ve analyzed large biological protein networks and shown how statistical signatures reveal what evolutionary laws can give rise to such graphs.
- In collaboration with faculty at Columbia’s chemistry department and NYU’s medical school, we’ve developed hierarchical Bayesian inference methods that can automate the analysis of thousands of time series data from single molecules. These techniques can identify the best model from models of varying complexity, along with the kinetic and biophysical parameters of interest to the chemist and clinician.
- Our current projects include, in collaboration with experts at Columbia’s medical school in pathogenic viral genomics, using machine learning methods to reveal whether a novel viral sequence may be carcinogenic or may lead to a pandemic. This research requires an abundant corpus of training data as well as close collaboration with the domain experts to ensure that the models exploit — and are interpretable in light of — the decades of bench work that has revealed what we now know of viral pathogenic mechanisms.
Throughout, our goals balance building models that are not only predictive but interpretable, e.g., revealing which sequence elements convey carcinogenicity or permit pandemic transmissibility.
Data science in health
More generally, we can apply big data approaches not only to biological examples as above but also to health data and health records. These approaches offer the possibility of, for example, revealing unknown lethal drug-drug interactions or forecasting future patient health problems; such models could have consequences for both public health policies and individual patent care. As one example, the Heritage Health Prize is a $3 million challenge ending in April 2013 “to identify patients who will be admitted to a hospital within the next year, using historical claims data.” Researchers at Columbia, both in SEAS and at Columbia’s medical school, are building the technologies needed for answering such big questions from big data.
The need for skilled data scientists
In 2011, the McKinsey Global Institute estimated that between 140,000 and 190,000 additional data scientists will need to be trained by 2018 in order to meet the increased demand in academia and industry in the United States alone. The multidisciplinary skills required for data science applied to such fields as health and biology will include:
- the computational skills needed to work with large datasets usually shared online;
- the ability to format these data in a way amenable to mathematical modeling;
- the curiosity to explore these data to identify what features our models may be built on;
- the technical skills which apply, extend, and validate statistical and machine learning methods; and most importantly,
- the ability to visualize, interpret, and communicate the resulting insights in a way which advances science. (As the mathematician Richard Hamming said, “The purpose of computing is insight, not numbers.”)
More than a decade ago the statistician William Cleveland, then at Bell Labs, coined the term “data science” for this multidisciplinary set of skills and envisioned a future in which these skills would be needed for more fields of technology. The term has had a recent explosion in usage as more and more fields — both in academia and in industry — are realizing precisely this future.
Chris Wiggins is an associate professor in the Department of Applied Physics and Applied Mathematics at Columbia University. This piece originally appeared in an issue of Columbia Engineering that announced Columbia’s new Institute for Data Sciences and Engineering (http://idse.columbia.edu). It’s republished with permission.
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