“Interpretable Deep Learning for Cancer Personalized Medicine”
Maria Rodriguez Martinez, PhD
Group Leader of Computational Systems Biology IBM Research, Zurich, Switzerland
John Tsang, Director of the Center for Systems and Engineering Immunology, Professor of Immunobiology and Biomedical Engineering
In recent years, deep learning models have resulted in outstanding breakthrough performances. However, many models behave as black boxes that can hide data biases, incorrect hypotheses or even software errors. In this talk, I will illustrate how interpretable deep learning models can achieve both high prediction accuracy and transparency. First, I will introduce multi-modal deep learning models that predict drug response while highlighting the genetic and chemical patterns that were more informative to make a prediction. I will also discuss how reinforcement learning approaches can facilitate the early phases of drug discovery and support the personalized design of new candidate compounds. Focusing next on T cell-based immunotherapies, I will present a model to predict the binding of T cell receptors and epitopes. This model can be coupled with an easy-to-use interpretable pipeline to extract the binding rules governing the T cell binding. These approaches are a first step towards the design and engineering of receptors of improved affinity. Finally, I will discuss how the integration of AI and mechanistic models is necessary to tackle many current computational challenges and enable the personalized design of new therapeutic interventions.
About the speaker:
Dr. María Rodrίguez Martίnez is the Technical Leader of Systems Biology at IBM Research Europe (Switzerland) and an associated member of the Department of Biology at ETH since 2014. A theoretical physicist by training, she became interested in the development of computational and statistical approaches to unravel cancer molecular mechanisms using high-throughput multi-omics datasets and single-cell molecular data. In recent years, her team has specialized in the development of AI approaches for personalized drug modeling. More recently, she is building multi-scale models of the immune system through a combination of deep learning and mechanistic models. Through this effort, her team has developed deep learning models to predict the specificity of T cell receptors and stochastic mechanistic models to recapitulate B cell development.
She is also quite active in the area of interpretable deep learning. Deep learning has achieved astounding performances in a broad range of disciplines, but breakthrough performances have often come at the price of a lack of information about the rules that govern a model’s decision. Interpretable deep learning aims to develop models that can not only make a prediction with high accuracy, but can also provide insight into the reasons underlying the prediction. On this area, her team has contributed several novel methods for different applications in computational biology, ranging from AI-driven protein modeling to the integration of image and RNA-Seq data modalities.
About the Center:
The newly established Yale Center for Systems and Engineering Immunology (CSEI) aims to bring together Yale faculty and trainees from diverse departments across Yale University, including the School of Medicine, School of Engineering and Applied Science, and the Faculty of Arts and Sciences to deliberate and collaborate on systems, quantitative, and synthetic immunology. The Center serves as an interdisciplinary home and meeting place for faculty, researchers, and students interested in developing a quantitative, predictive understanding of the immune system and in advancing technologies and computational approaches for systematic engineering of synthetic immune molecules, cells, and systems to empower both basic understanding and biomedical applications. The Center also aims to help enable computational, data, and technology intensive studies involving the immune system, including those that study the interactions between the immune system and the (patho)physiology of all organ systems in health and disease. Towards these goals, the Center is launching a seminar and chalk talk series and will be recruiting new faculty and staff to complement existing strengths at Yale. The CSEI is supported by the Yale School of Medicine Dean’s office and the Department of Immunobiology.
Date: January 31, 2023
Time: 1:00 – 2:00 pm
Location: Brady Auditorium (BML 131), 310 Cedar Street