Co-founding and Organizing Conferences, Workshops, and Tutorials

Research Tutorials

Towards Precision Medicine with Graph Representation Learning (ISMB 2022)

Graph representation learning has matured immensely as a field within the last few years. Graph machine learning approaches, also known as geometric deep learning, or graph neural networks has become widely used in biomedical applications. This tutorial surveys impact areas in precision medicine (e.g., modeling disease progression, candidate biomarker discovery for targeted therapies, rapid disease diagnostics, treatment regimen recommendations) and highlights new opportunities enabled by these approaches.

This tutorial was presented at the International Conference on Intelligent Systems for Molecular Biology (ISMB).

biomedgraphml-ismb

Machine Learning for Drug Development (IJCAI 2021)

Machine learning methods leverage big datasets to support decision-making in all stages of drug development, predict how drugs affect the human body and how they interact with each other, and seek ways to boost clinical trials and detect unwanted side effects. This tutorial covers generative modeling, reinforcement learning, and representation learning with a focus on theoretical foundations of methods and their use for key drug-related problems.

A variety of machine learning methods are demonstrating their utility at all stages of drug development. These methods use big datasets created from high-throughput screening data and allow prediction of bioactivities for targets and molecular properties, identification of new molecules and repurposing of old drugs with increased levels of accuracy.

We have only just begun to realize the potential of these techniques. If methods were available for all aspects of drug development, they could be used seamlessly to predict whether a chemical compound is likely to ultimately become a drug used in patients. Much research needs to be done before this vision can be realized, modern machine learning may have a fundamental impact on the way drug development is done.

The general process of drug development involves five steps. In short, molecular compounds are filtered through a progressive series of tests, which determine their properties, toxicity, and effectiveness for later stages. Machine learning is increasingly being used to accelerate each of the steps, creating opportunities for reducing resources and time needed to develop new drugs. In this tutorial, we cover key problems in drug development that are amenable to machine learning. In doing so, we present a toolbox of AI algorithms for end-to-end drug development.

This tutorial was presented at the International Joint Conference on Artificial Intelligence (IJCAI).

drugml-ijcai

Deep Learning for Network Biology (ISMB 2018)

Networks are ubiquitous in biology where they encode connectivity patterns at all scales of organization, from molecular to the biome. This tutorial investigates key advancements in representation learning for networks over the last few years, with an emphasis on fundamentally new opportunities in network biology enabled by these advancements.

Biological networks are powerful resources for the discovery of interactions and emergent properties in biological systems, ranging from single-cell to population level. Network approaches have been used many times to combine and amplify signals from individual genes, and have led to remarkable discoveries in biology, including drug discovery, protein function prediction, disease diagnosis, and precision medicine. Furthermore, these approaches have shown broad utility in uncovering new biology and have contributed to new discoveries in wet laboratory experiments.

Mathematical machinery that is central to these approaches is machine learning on networks. The main challenge in machine learning on networks is to find a way to extract information about interactions between nodes and to incorporate that information into a machine learning model. To extract this information from networks, classic machine learning approaches often rely on summary statistics (e.g., degrees or clustering coefficients) or carefully engineered features to measure local neighborhood structures (e.g., network motifs). These classic approaches can be limited because these hand-engineered features are inflexible, they often do not generalize to networks derived from other organisms, tissues and experimental technologies, and can fail on datasets with low experimental coverage.

Recent years have seen a surge in graph neural network (GNN) approaches that automatically learn to encode network structure into low-dimensional representations, using transformation techniques based on deep learning and nonlinear dimensionality reduction. The idea behind these representation learning approaches is to learn a data transformation function that maps nodes to points in a low-dimensional vector space, also termed embeddings. Representation learning methods have revolutionized the state-of-the-art in network science and the goal of this tutorial is to open the door for these methods to computational biology and bioinformatics.

This tutorial was presented at the International Conference on Intelligent Systems for Molecular Biology (ISMB).

deepnetbio-ismb

Biomedical Data Fusion (EMBC and [BC]^2 2015)

Because of the complex and interconnected nature of biomedical systems, any single model trained on any single dataset can touch only a small part of the entire biomedical knowledge. It is thus critical to integrate diverse sources of information to gain a comprehensive understanding of the system.

New technologies have enabled the investigation of biology and human health at an unprecedented scale and in multiple dimensions. These dimensions include a myriad of properties describing genome, epigenome, transcriptome, microbiome, phenotype, and lifestyle. No single data type, however, can capture the complexity of all the factors relevant to understanding a phenomenon such as a disease. Integrative methods that combine data from multiple technologies have thus emerged as critical statistical and computational approaches.

The key challenge in developing such approaches is the identification of effective models to provide a comprehensive and relevant systems view. An ideal method can answer a biological or medical question, identifying important features and predicting outcomes, by harnessing heterogeneous data across several dimensions of biological variation.

This tutorial was presented at the International Engineering in Medicine and Biology Conference (EMBC) and at the Basel Compuational Biology Conference ([BC]^2).

biomedical-data-fusion


International Workshops and Conferences

Graph Representations and Algorithms in Biomedicine (PSB 2023)

Connectivity is a fundamental property of biological systems: on the cellular level, proteins interact with each other to form protein-protein interaction networks; on the organism level, neurons are arranged in a network; and on a community level, species can have complex relationships with one another that drive the development of an ecosystem. Graphs, mathematical representations modeling entities as vertices and their relationships as edges, have proved useful for understanding biological systems that naturally have such a network structure. Graph representations and algorithms (often in combination with machine learning techniques) can be used to organize massive amounts of related (and sometimes heterogenous or unstructured) data, and to ultimately to identify patterns that represent novel biological insights.

This session will encompass modern developments in graph theory, computational topology, and graph machine learning applied to various fields of biomedicine.

AI for Science (ICML 2022)

Machine learning is poised to transform scientific discovery. Despite this promise, critical challenges stifle algorithmic and scientific innovation across scientific disciplines.

This workshop brings those challenges to the forefront and discusses which are likely/unlikely to have a broad impact on scientific discovery. With hundreds of AI/ML scientists beginning projects in this area, the workshop brings them together to facilitate community building and consolidate the fast growing area of AI4Science into a vibrant research field.

This workshop is presented at the International Conference on Machine Learning (ICML).

Trustworthy AI for Healthcare (AAAI 2022)

AI for healthcare has emerged into a very active research area in the past few years and has made significant progress. For example, AI methods have achieved human-level performance in skin cancer classification, diabetic eye disease detection, chest radiograph diagnosis, sepsis treatment.

While current results are encouraging, few clinical AI solutions are deployed in hospitals or routinely used in the clinic. The major problem is that existing clinical AI methods are not sufficiently trustworthy. Black-box methods generate decisions that are difficult to understand and interpret. Furthermore, existing solutions are sensitive to small perturbations and adversarial attacks, which raises security and privacy concerns. Further, methods can produce results biased against certain populations. This workshop addresses these challenges and puts forward recommendations on how to make clinical AI solutions more trustworthy.

This workshop was presented at the AAAI Conference on Artificial Intelligence (AAAI 2022).

Workshop on Graph Learning Benchmarks (The Web Conference 2022)

While graph neural networks achieve promising performance on node classification, graph classification, and link prediction, reported performance gains can only be verified and compared within a limited set of publicly available benchmark datasets. The lack of diversity in benchmark datasets may have biased the development of graph representation learning techniques towards narrow directions.

By crowdsourcing tasks and datasets, the workshop has increased the diversity of graph learning benchmarks, identified open questions in graph representation learning, and deepened the synergy between graph ML algorithms and benchmark datasets.

The workshop was organized at the Web Conference (WWW).

AI for Science (NeurIPS 2021)

Machine learning has advanced a wide array of scientific disciplines and addressed many problems that previously could not be tackled computationally. Despite this promise, several key challenges remain open, and this workshop brings those gaps to the foreground of AI research.
  • Gap 1: Unrealistic methodological assumptions. While ML researchers strive for methodology advances, they often make unrealistic assumptions that limit real-world adoption. For example, most state-of-the-art molecule generation ML models generate molecules that have low synthesizability.

  • Gap 2: Overlooked scientific questions. Scientific communities contend with crucial and unsolved problems, but they are not yet formulated as solvable ML tasks and are thus overlooked by the ML community.

  • Gap 3: Limited exploration at the intersection of multiple disciplines. Solutions to grand challenges often stretch across multiple disciplines. For example, protein structure prediction requires collaboration across physics, chemistry and biology.

  • Gap 4: Science of science. Core principles of the scientific method have not changed since the 17th century. Can AI reason about the organizing principles of our world in a way that is complementary to the hypothesis-experiment cycle to understand a phenomenon?

  • Gap 5: Responsible use and development of AI for science. Interest in ML across scientific disciplines has surged, but few ML models have transitioned into practical scientific applications. We plan to present a roadmap and ultimately guidelines for accelerating the translation of ML in science. Translation requires a team of engaged stakeholders and a systematic process from the beginning (problem formulation) to the end (widespread deployment) of ML-based research lifecycle.

This workshop was presented at the International Conference on Neural Information Processing Systems (NeurIPS).

Trustworthy AI for Healthcare (AAAI 2021)

Artificial intelligence for healthcare has emerged as an active research area that has made considerable progress, including achieving human-level performance for skin cancer classification, diabetic eye disease detection, chest radiograph diagnosis, and sepsis treatment. While the trends are encouraging, many open challenges prevent us from directly deploying AI solutions in hospitals and clinical environments. A major open problem is the lack of trust of biomedical practitioners in AI methods. Many AI methods make predictions in a black-box way, making decisions challenging to understand and interpret. Further, today's methods are sensitive to small perturbations and adversarial attacks, raising numerous security and privacy concerns. Finally, AI methods learn to make decisions based on training data, which can include biased human decisions or reflect historical or social inequities. These challenges raise numerous trustworthy issues that we need to address to realize the potential of AI in healthcare.

This workshop was presented at the AAAI Conference on Artificial Intelligence (AAAI 2021).

AI in Health: Transferring and Integrating Knowledge for Better Health (The Web Conference 2021)

Rich healthcare data connected by semantic relationships and integrated into knowledge graphs can drive biomedical discovery. Biomedical knowledge graphs can support better cohort identification for clinical trials, risk prediction, precision diagnosis, and can inform new and better decision support workflows. Dramatic increase of healthcare data offers unprecedented opportunities for evidence-based care, yet challenges related to interoperability, learning, and reasoning over healthcare data remain open.

This workshop was presented at the Web Conference (WWW).

National Symposium on Drug Repurposing for Future Pandemics (2020)

Pandemics demand safe and effective therapies developed and deployed at an unprecedented speed. This symposium, organized on behalf of the National Science Foundation (NSF), provides a forum for scientists and researchers from a variety of fields relevant to therapeutics. Participants discuss ways to expedite the development of therapies by compressing years of work into months or even weeks through automation, artificial intelligence and machine learning, novel data sources, and most recent biotechnology advancements.

futuretx20-symposium

The symposium brings together leading experts in computer science, biology, statistics, medicine, automation, and regulation. While these areas of expertise are necessary for rapid therapeutic innovation, there is seldom an opportunity for these experts to interact with each other.

Bearing in mind new opportunities and pressing challenges, the symposium provides a roadmap and put forward recommendations on transforming today’s tools into ready-to-use solutions to fight future pathogens.

We announce a new initiative, Therapeutics Data Commons (TDC), at the symposium [Slides].

futuretx20-TDC

Graph Representation Learning and Beyond (ICML 2020)

Recent years have seen a surge in research on graph representation learning, including techniques for deep graph embeddings, generalizations of CNNs to graph-structured data, and neural message-passing approaches. These advances in graph neural networks and related techniques have led to new state-of-the-art results in numerous domains: chemical synthesis, 3D-vision, recommender systems, question answering, continuous control, self-driving, and social network analysis.

This workshop was presented at the International Conference on Machine Learning (ICML).

Representation Learning on Graphs and Manifolds (ICLR 2019)

Many scientific fields study data with an underlying graph or manifold structure—such as social networks, sensor networks, biomedical knowledge graphs, and meshed surfaces in computer graphics. Recent years have seen a surge in research on these problems—often under the umbrella terms of graph representation learning and geometric deep learning.

The need for new optimization methods and neural network architectures that can accommodate these relational and non-Euclidean structures is becoming increasingly clear. In parallel, there is a growing interest in how we can leverage insights from these domains to incorporate new kinds of relational and non-Euclidean inductive biases into deep learning.

This workshop was presented at the International Conference on Learning Representations (ICLR).

decagon-architecture


Research and Scholarship Meetings

Therapeutics Data Commons User Group Meeting (2022)

Therapeutics Data Commons is an open-science initiative started at Harvard with AI/ML-ready datasets and ML tasks for therapeutics. TDC provides an ecosystem of tools, leaderboards, and community resources, including data functions, strategies for model benchmarking and comparison, meaningful data splits, data processors, public leaderboards, and molecule generation oracles. All resources are integrated via an [open Python library.](https://github.com/mims-harvard/TDC)

The lack of high-quality benchmarks impedes the advancement of ML tools for drug discovery. To this end, TDC supports the development of novel ML theory and methods, with a strong bent towards developing the mathematical foundations of which ML algorithms are most suitable for drug discovery applications and why. TDC contains benchmarks for therapeutics ML tasks, including molecular property prediction, molecular interaction prediction, and molecular optimization, all accompanied by extensive programmatic support and leaderboards.

The first live user meeting outlined opportunities for how to engage with the TDC community, provided technical background and demos on how TDC supports ML for therapeutics and molecules.

TDC User Meetup

PhD Forum (ECML/PKDD 2020)

PhD Forum provides an environment for junior PhD students to exchange ideas and experiences with peers in an interactive atmosphere and to get constructive feedback from senior researchers in data science, machine learning, and related areas.

This meeting took place at the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD).

Latest News

May 2022:   George Named the 2022 Wojcicki Troper Fellow

May 2022:   New preprint on PrimeKG

New preprint on building knowledge graphs to enable precision medicine applications.

Apr 2022:   Webster on the Cover of Cell Systems

Webster is on the cover of April issue of Cell Systems. Webster uses cell viability changes following gene perturbation to automatically learn cellular functions and pathways from data.

Apr 2022:   NASA Space Biology

Dr. Zitnik will serve on the Science Working Group at NASA Space Biology.

Mar 2022:   Yasha's Graduate Research Fellowship

Yasha won the National Defense Science and Engineering Graduate (NDSEG) Fellowship. Congratulations!

Mar 2022:   AI4Science at ICML 2022

We are excited to be selected to organize the AI4Science meeting at ICML 2022. Stay tuned for details. http://www.ai4science.net/icml22

Mar 2022:   Graph Algorithms in Biomedicine at PSB 2023

Excited to be organizing a session on Graph Algorithms at PSB 2023. Stay tuned for details.

Mar 2022:   Multimodal Learning on Graphs

New preprint! We introduce REMAP, a multimodal AI approach for disease relation extraction and classification. Project website.

Feb 2022:   Explainable Graph AI on the Capitol Hill

Owen has been selected to present our research on explainable biomedical AI to members of the US Congress at the “Posters on the Hill” symposium. Congrats Owen!

Feb 2022:   Graph Neural Networks for Time Series

Hot off the press at ICLR 2022. Check out Raindrop, our graph neural network with unique predictive capability to learn from irregular time series. Project website.

Feb 2022:   Biomedical Graph ML Tutorial Accepted to ISMB

Excited to present a tutorial at ISMB 2022 on graph representation learning for precision medicine. Congratulations, Michelle!

Feb 2022:   Marissa Won the Gates Cambridge Scholarship

Marissa Sumathipala is among the 23 outstanding US scholars selected be part of the 2022 class of Gates Cambridge Scholars at the University of Cambridge. Congratulations, Marissa!

Jan 2022:   Inferring Gene Multifunctionality

Jan 2022:   Deep Graph AI for Time Series Accepted to ICLR

Paper on graph representation learning for time series accepted to ICLR. Congratulations, Xiang!

Jan 2022:   Probing GNN Explainers Accepted to AISTATS

Jan 2022:   Marissa Sumathipala selected as Churchill Scholar

Marissa Sumathipala is selected for the prestigious Churchill Scholarship. Congratulations, Marissa!

Jan 2022:   Therapeutics Data Commons User Meetup

We invite you to join the growing open-science community at the User Group Meetup of Therapeutics Data Commons! Register for the first live user group meeting on Tuesday, January 25 at 11:00 AM EST.

Jan 2022:   Workshop on Graph Learning Benchmarks

Dec 2021:   NASA: Precision Space Health System

Human space exploration beyond low Earth orbit will involve missions of significant distance and duration. To effectively mitigate myriad space health hazards, paradigm shifts in data and space health systems are necessary to enable Earth independence. Delighted to be working with NASA and can share our recommendations!

Zitnik Lab  ·  Harvard  ·  Department of Biomedical Informatics