Cracking the code of how diseases affect the body

ARA recipient Marinka Zitnik is focused on how machine learning can enable accurate diagnoses and the development of new treatments and therapies.

Early in her career, computer scientist Marinka Zitnik confronted a biomedical mystery: among 12,000 genes, which handful played a role in the response of a model organism to bacterial infection? A genuine needle-in-a-haystack situation.

Marinka Zitnik portrait.png
Marinka Zitnik, an assistant professor of biomedical informatics at the Harvard Medical School, whose Amazon Research Award supports her work on unlocking the potential of AI-augmented drug discovery at the global scale through the online platform Therapeutics Data Commons.

But when Zitnik fed the biomedical data into a machine learning algorithm of her own devising, it predicted eight genes most likely to be involved. When those candidates were tested in the lab, the research team found that six of them were indeed implicated in the infection. Her method had proven sensationally successful.

"As someone who was trained in computer science at the time, it was so rewarding to make an impact in another area,” says Zitnik. “It was a turning point for me.”

That turning point, in 2013, led to a decade of research in machine learning and to Zitnik's current role as assistant professor of biomedical informatics at Harvard Medical School. At Harvard's Zitnik Lab, she is focused on how machine learning can enable accurate diagnoses and the development of new treatments and therapies. And with the support of an Amazon Research Award, she is working to unlock the potential of AI-augmented drug discovery at the global scale through the online platform Therapeutics Data Commons.

Today, of course, bioinformatics is an established and growing discipline. But during Zitnik’s final year at high school it was a magic word, one she hadn’t heard before, that suddenly revealed how she could combine her passion for computers, programming, and mathematics with her ambition to make a big impact on society.

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“I stumbled across a lecture given by a university recruiter, and I learned this word. Bioinformatics combines computation and biology. It was an emerging area that really sparked my interest,” says Zitnik. Following her subsequent degree in computer science and mathematics at the University of Ljubljana, Slovenia, she stayed and started a PhD in computer science in 2012, all the while with medicine in mind.

“I wanted to deeply understand the complex problems in biology and medicine that I could use computation to help solve,” Zitnik says.

Bottlenecks and challenges

Early in Zitnik’s PhD, she published several machine learning papers that were read by scientists at a variety of biomedical institutions. Many reached out to invite her to their labs to collaborate in applying her algorithms to their data. During her PhD, Zitnik joined forces with clinicians, biomedical researchers, geneticists, and computer scientists around the world, including Stanford University and Imperial College London.

“I wanted to learn about the process of fundamental biological discovery in a lab — the bottlenecks and the challenges,” she says.

One of these collaborations — with Baylor College of Medicine in Houston, Texas — was particularly encouraging: the 12,000-gene challenge. The conventional approach would have required many thousands of screening experiments, testing each gene in turn. The success of Zitnik’s algorithms meant the saving of a great deal of time and resources.

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“That was the first time I saw that coupling AI predictions with experimental biological work in the lab can improve experimental yield by an order of magnitude,” says Zitnik.

Fast forward to 2019, when Zitnik arrived at Harvard University to set up her lab. Zitnik focused on two closely linked areas of medicine that could also benefit from AI. One is how machine learning can enable an accurate diagnosis for a patient based on a wide variety of information, from their genetic code and blood test results to their medical history and lifestyle data. The second area involves identifying and developing possible treatments and therapies for these diagnoses.

Therapeutics Data Commons

More than this, though, Zitnik wanted to unlock the potential of AI-augmented medicine at the global scale. From her early work with the biomedical community, she understood all too well the difficulty in accessing and curating high-quality medical data to train ML models. She addressed these twin challenges head on, leveraging Amazon Elastic Compute Cloud (EC2) and AWS ML deployment tools via her Amazon Research Award to launch Therapeutics Data Commons (TDC), an international initiative to access and evaluate AI capability across therapeutic modalities and stages of discovery.

At its core, TDC is a collection of open-source data sets and state-of-the-art ML models focused on drug discovery and development, accompanied by a broader ecosystem of resources and tools that include benchmarking and leader boards for cutting-edge ML models.

“It’s a meeting point between biomedical and biochemical researchers, and machine learning scientists,” says Zitnik. “It’s a thriving community.”

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TDC is the largest open-source platform of its kind in the world. Zitnik runs it with collaborating institutions including MIT, Stanford University, Georgia Institute of Technology, Cornell University, University of Illinois Urbana-Champaign, and Carnegie Mellon University, and with additional support from the pharmaceutical industry and tech companies. TDC covers the entire process of drug discovery and development, from identifying potentially therapeutic molecules to the optimizing and planning of laboratory experiments.

The platform holds data from anonymized electronic health records, medical imaging, genomics, clinical trials data, and lots more. Biomedical researchers can use TDC’s data, or bring their own data and challenges, and collaborate with ML scientists to increase the speed of drug discovery while also reducing the otherwise enormous cost of bringing new drugs to market. It has already been used by more than 200,000 scientists worldwide, says Zitnik.

Help for rare diseases

Zitnik is also keen to use her technology to help patients and clinicians working on rare diseases. There are over 7,000 rare diseases in the world, says Zitnik. Each of them has a small number of known cases, but collectively they affect many people. Could AI help here?

To develop a diagnostic model for a common disease typically requires data from thousands of patients, labelled with that diagnosis. For rare diseases, that labelled patient data simply doesn’t exist. “This problem cannot be solved by throwing more money at it,” says Zitnik. “It requires a new way of thinking.”

Instead, Zitnik and her team, which includes postdoctoral fellow Emily Alsentzer and graduate researcher Michelle Li, are incorporating medical principles and prior scientific knowledge about biological interactions, chemistry, genetics, patient symptoms, and drug interactions into the neural architecture of their models.

“This allows us to train sophisticated deep learning models using very little amounts of labelled patient data, and sometimes no patient data at all,” says Zitnik.

A collaboration with a Harvard-led study called the Undiagnosed Diseases Network (UDN) has shown that the approach works. Someone with a rare genetic disease that has defied diagnosis at the local level can be referred to the UDN’s network of clinical and research experts across 12 U.S. clinical sites. A diagnosis can resolve the burden of uncertainty for the patient and hopefully unlock the possibility of treatments. Of the 2,500 participants so far accepted into the UDN study, 627 have been successfully diagnosed — each case a hard-fought win.

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When Zitnik’s team applied their model to the medical data of 465 of these patients — a data set that excluded their actual diagnosis — the results were striking. The model was asked to predict for each patient the genes mostly likely responsible for their illness. For three-quarters of the patients, the disease-causing gene was in the model’s top five predictions.

“The next stage is to use it in real-world settings to assist the clinical teams in the evaluation of undiagnosed patients,” says Zitnik.

The tool has drawn considerable interest from the medical community, says Zitnik. She is planning pilot studies with clinics in Boston and Israel that are not part of the UDN to further evaluate the model as a diagnostic recommendation tool for new cases. Zitnik is also in discussions with several patient-led foundations centered around individual rare diseases, with the goal of providing them with a suite of user-friendly tools.

That is something Amazon Web Services supports. “When we are looking to deploy a model in biomedical or clinical settings, we use SageMaker,” Zitnik says. Amazon SageMaker can be used to turn ML models into standalone tools for public release, for example, or to place algorithms in cloud-based containers for sharing them with collaborators.

The power of the cloud for biomedical data

Cloud computing more broadly is critical to the work in the Zitnik lab.

“We need to train our models repeatedly on many different kinds of health data, to make sure they perform well across diverse patient populations, diverse chemical structures and so on, even if the input data is relatively messy,” says Zitnik. Her Amazon Research Award provided AWS credits for access to the high-powered parallel computing required by these training-hungry models.

In addition to the launch of TDC, Zitnik’s Amazon award supported discrete research projects. In 2021, as the COVID-19 pandemic raged around the world, Zitnik and her team wanted to know how effective AI methods could be at identifying existing drugs that could be repurposed to treat emerging pathogens. Identifying drugs already on the market or in late-stage clinical trials can save many years, and potentially billions of dollars, compared with developing a drug from scratch.

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Zitnik’s team first trained a geometric deep learning model on the human interactome — the complete network of physical interactions between proteins in the human body. These networks tell us what parts of human cells’ machinery are affected by a given drug molecule.

Once the model was trained, they fed it data on over 7,500 existing drugs and their mechanisms of action. Of these drugs, the model predicted and ranked 6,340 candidate drugs. Biomedical researchers screened the top 918 suggestions on cells infected with COVID-19 and found 77 drugs that had a strong or weak effect on the virus. They used these results to fine-tune the model’s predictions, before finally screening the top-ranked drugs in human cells. They identified six drugs that reduced viral infection. Among these, four could, in principle, be repurposed to treat COVID-19.

“It’s an exciting example of how AI can accelerate drug discovery and development. We were able to compress the timeline of this kind of research — from data collection to final models and predictions being tested in the lab — from years to months,” says Zitnik. Three months, in this case.

This is impressive in itself, but the experiment also revealed another aspect of the power of AI approaches.

Cascading network effects

A well-established strategy for drug discovery is to exploit molecular docking. If an infecting pathogen needs to dock with a particular protein on the surface of human cells to proliferate, a therapeutic molecule that docks with that protein instead could block the action of the pathogen. Indeed, Zitnik’s model did identify one drug that bound to the same proteins targeted by SARS-CoV-2. But here’s the kicker — it also found 76 drugs that successfully reduced viral infection through indirect systemic effects.

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“One of the biggest outcomes of the work was the discovery of this group of drugs that seem to work through cascading network effects, indirectly impacting the proteins the virus attacks,” says Zitnik. “We call these network drugs. Without algorithms such as graph neural networks, which can make indirect observations and inferences using principles grounded in biomedical knowledge, we would not be able to identify such drugs.”

This new way to approach discovery, powered by biomedical AI, excites Zitnik for the future. She sees the potential for such tools to generate more accurate scientific hypotheses tailored to individual cells, diseases, and patients, and to help bridge the gap between laboratory and clinical settings:

“I can't wait to see how these developments will continue to shape our world.”

Research areas

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Have you ever ordered a product on Amazon and when that box with the smile arrived you wondered how it got to you so fast? Have you wondered where it came from and how much it cost Amazon to deliver it to you? We are looking for a Senior Data Scientist who will be responsible to develop cutting-edge scientific solutions to optimize our Pan-European fulfillment strategy, to maximize our Customer Experience and minimize our cost and carbon footprint. You will partner with the worldwide scientific community to help design the optimal fulfillment strategy for Amazon. You will also collaborate with technical teams to develop optimization tools for network flow planning and execution systems. Finally, you will also work with business and operational stakeholders to influence their strategy and gather inputs to solve problems. To be successful in the role, you will need deep analytical skills and a strong scientific background. The role also requires excellent communication skills, and an ability to influence across business functions at different levels. You will work in a fast-paced environment that requires you to be detail-oriented and comfortable in working with technical, business and technical teams. Key job responsibilities - Design and develop mathematical models to optimize inventory placement and product flows. - Design and develop statistical and optimization models for planning Supply Chain under uncertainty. - Manage several, high impact projects simultaneously. - Consult and collaborate with business and technical stakeholders across multiple teams to define new opportunities to optimize our Supply Chain. - Communicate data-driven insights and recommendations to diverse senior stakeholders through technical and/or business papers. We are open to hiring candidates to work out of one of the following locations: London, GBR
US, VA, Arlington
Are you looking to work at the forefront of Machine Learning and AI? Would you be excited to apply cutting edge Generative AI algorithms to solve real world problems with significant impact? The Generative AI Innovation Center at AWS is a new strategic team that helps AWS customers implement Generative AI solutions and realize transformational business opportunities. This is a team of strategists, data scientists, engineers, and solution architects working step-by-step with customers to build bespoke solutions that harness the power of generative AI. The team helps customers imagine and scope the use cases that will create the greatest value for their businesses, select and train and fine tune the right models, define paths to navigate technical or business challenges, develop proof-of-concepts, and make plans for launching solutions at scale. The GenAI Innovation Center team provides guidance on best practices for applying generative AI responsibly and cost efficiently. You will work directly with customers and innovate in a fast-paced organization that contributes to game-changing projects and technologies. You will design and run experiments, research new algorithms, and find new ways of optimizing risk, profitability, and customer experience. We’re looking for Data Scientists capable of using GenAI and other techniques to design, evangelize, and implement state-of-the-art solutions for never-before-solved problems. A key focus of this role is GenAI model customization using techniques such as fine-tuning and continued pre-training to help customers build differentiating solutions with their unique data. Key job responsibilities As a Data Scientist, you will: Collaborate with AI/ML scientists and architects to research, design, develop, and evaluate cutting-edge generative AI algorithms to address real-world challenges Interact with customers directly to understand the business problem, help and aid them in implementation of generative AI solutions, deliver briefing and deep dive sessions to customers and guide customer on adoption patterns and paths to production Create and deliver best practice recommendations, tutorials, blog posts, sample code, and presentations adapted to technical, business, and executive stakeholder Provide customer and market feedback to Product and Engineering teams to help define product direction About the team Sales, Marketing and Global Services (SMGS) AWS Sales, Marketing, and Global Services (SMGS) is responsible for driving revenue, adoption, and growth from the largest and fastest-growing small- and mid-market accounts to enterprise-level customers, including the public sector. The AWS Global Support team interacts with leading companies and believes that world-class support is critical to customer success. AWS Support also partners with a global list of customers that are building mission-critical applications on top of AWS services. The Professional Services team is part of Global Services. About AWS Diverse Experiences AWS values diverse experiences. Even if you do not meet all of the qualifications and skills listed in the job description, we encourage candidates to apply. If your career is just starting, hasn’t followed a traditional path, or includes alternative experiences, don’t let it stop you from applying. Why AWS? Amazon Web Services (AWS) is the world’s most comprehensive and broadly adopted cloud platform. We pioneered cloud computing and never stopped innovating — that’s why customers from the most successful startups to Global 500 companies trust our robust suite of products and services to power their businesses. Inclusive Team Culture Here at AWS, it’s in our nature to learn and be curious. Our employee-led affinity groups foster a culture of inclusion that empower us to be proud of our differences. Ongoing events and learning experiences, including our Conversations on Race and Ethnicity (CORE) and AmazeCon (gender diversity) conferences, inspire us to never stop embracing our uniqueness. Mentorship & Career Growth We’re continuously raising our performance bar as we strive to become Earth’s Best Employer. That’s why you’ll find endless knowledge-sharing, mentorship and other career-advancing resources here to help you develop into a better-rounded professional. Work/Life Balance We value work-life harmony. Achieving success at work should never come at the expense of sacrifices at home, which is why we strive for flexibility as part of our working culture. When we feel supported in the workplace and at home, there’s nothing we can’t achieve in the cloud. We are open to hiring candidates to work out of one of the following locations: Arlington, VA, USA | Denver, CO, USA | Herndon, VA, USA | New York, NY, USA | Santa Clara, CA, USA | Seattle, WA, USA | Washington Dc, DC, USA