The Zitnik Lab opened doors in December 2019!
We investigate applied machine learning with a current focus on large interconnected data in science and medicine—i.e., rich networks of interactions between proteins, drugs, diseases, and patients. We leverage data at the scale of billions of interactions and invent new methods that blend machine learning with data science and statistics.
We use our methods to answer questions in biology, such as how Darwinian evolution changes molecular networks, and how data-driven algorithms can accelerate and automate scientific discovery. We use the methods to solve high-impact problems in medicine, such as what drugs and combinations of drugs are safe for patients, what molecules will treat what diseases, and how newborns are transferred between hospitals and how these transfers influence outcomes.
Our world is interconnected, from the molecular level to the level of connections between diseases in a person, and all the way to the societal level encompassing human interactions within a society. These interactions at different levels give rise to a bewildering degree of complexity.
To disentangle the complexity, science inextricably relies on the existence of scientific instruments. While in the past science used physical instruments to facilitate the discoveries, modern science needs the new kind of instruments, which will, we postulate, in a vital way be optimized for learning and reasoning from data.
The overarching goal of our research is to develop the next generation of machine learning for data in medicine and science. Our research realizes an end-to-end scientific approach in which we:
- Invent ways to combine rich, heterogeneous data in their broadest sense to reduce redundancy and uncertainty and to make them amenable to comprehensive analyses.
- Develop methods for reasoning over rich, interconnected data, and design architectures for learning actionable representations.
- Translate machine learning research into innovative applications and solutions for burning biomedical questions.
Our research proves that this approach not only opens up new avenues for understanding nature, analyzing health, and developing new medicines to help people but can impact the way predictive modeling is performed today at the fundamental level.