Machine Learning for Medicine and Science


Open positions

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:

  1. Invent ways to combine rich, heterogeneous data in their broadest sense to reduce redundancy and uncertainty and to make them amenable to comprehensive analyses.
  2. Develop methods for reasoning over rich, interconnected data, and design architectures for learning actionable representations.
  3. 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.

Read about our research. . .

Latest News

Sep 2021:   Major New Release of TDC

We are very excited to announce a major release of Therapeutics Data Commons! In the 0.3.0 release we restructured the codebase, simplified the backend and kept user interfaces the same. We also provide detailed documentation for our TDC package.

Aug 2021:   Trustworthy AI for Healthcare at AAAI

We will be organizing a meeting on Trustworthy AI for Healthcare at AAAI 2022. Stay tuned for details and call for papers.

Aug 2021:   New Paper on Therapeutics Data Commons

Our latest paper on Therapeutics Data Commons: Machine Learning Datasets and Tasks for Drug Discovery and Development will appear at NeurIPS. We are excited to contribute novel datasets and benchmarks in the broad area of therapeutics.

Aug 2021:   AI for Science at NeurIPS

We are organizing the AI for Science workshop at NeurIPS 2021 and have a stellar lineup of invited speakers.

Aug 2021:   Best Poster Award at ICML Comp Biology

Congratulations to Michelle for winning the Best Poster Award for her work on deep contextual learners for protein networks at the ICML Workshop on Computational Biology.

Jul 2021:   Best Paper Award at ICML Interpretable ML

Our short paper on Interactive Visual Explanations for Deep Drug Repurposing received the Best Paper Award at the ICML Interpretable ML in Healthcare Workshop. Stay tuned for more news on this evolving project.

Jul 2021:   Five presentations at ICML 2021

Jun 2021:   Theory and Evaluation for Explanations

We introduce the first axiomatic framework for theoretically analyzing, evaluating, and comparing GNN explanation methods. We formalize key properties that all methods should satisfy to generate reliable explanations: faithfulness, stability, and fairness.

Jun 2021:   Deep Contextual Learners for Protein Networks

New preprint on contextualized protein embeddings aims to characterize genes with disease-specific interactions and elucidate disease manifestation in specific cell types.

May 2021:   New Paper Accepted at UAI

Our unified framework for fair and stable graph representation learning has just been accepted at UAI. We establish a theoretical connection between counterfactual fairness and stability and use it in a framework that can be used with any GNN to learn fair and stable embeddings.

Apr 2021:   Hot Off the Press: COVID-19 Repurposing in PNAS

Hot off the press! We deployed AI/ML and network medicine algorithms to rank 6,340 drugs for their expected efficacy against SARS-CoV-2. We screened in human cells the top-ranked drugs, identifying six drugs that reduced viral infection, four of which could be repurposed to treat COVID-19.

Apr 2021:   Representation Learning for Biomedical Nets

In our survey on representation learning for biomedical networks we discuss how long-standing principles of network biology and medicine provide the conceptual grounding for representation learning, explain its successes, and inform future advances.

Mar 2021:   Receiving Amazon Research Award

We are excited about receiving Amazon Faculty Research Award on Actionable Graph Learning for Finding Cures for Emerging Diseases. Thank you to Amazon Science for supporting our research.

Mar 2021:   Michelle's Graduate Research Fellowship

Michelle M. Li won the NSF Graduate Research Fellowship Award. Congratulations!

Mar 2021:   Hot Off the Press: Multiscale Interactome

Hot off the press! We develop a multiscale interactome approach to explain disease treatments. The approach can predict drug-disease treatments, identify proteins and biological functions related to treatment, and identify genes that alter treatment’s efficacy and adverse reactions.

Mar 2021:   Graph Networks in Computational Biology

We are excited to share slides from our recent lecture on Graph Neural Networks in Computational Biology, which we gave at Stanford ML for Graphs course.

Mar 2021:   Fair and Stable Graph Representation Learning

We are thrilled to share the latest preprint on fair and stable graph representation learning.

Feb 2021:   New Preprint on Therapeutics Data Commons

Jan 2021:   An Algorithmic Approach to Patient Safety

The new algorithmic approach investigates population-scale patient safety data and reveals inequalities in adverse events before and during COVID-19 pandemic.

Jan 2021:   Workshop on AI in Health at the Web Conference

We are excited to co-organize Workshop on AI in Health: Transferring and Integrating Knowledge for Better Health at the Web (WWW) conference. The call for papers is open! We also announce the AI in Health Data Challenge.

Zitnik Lab  ·  Harvard  ·  Department of Biomedical Informatics