Wistar welcomes Dr. Noam Auslander as an assistant professor in the Cancer Center’s Molecular & Cellular Oncogenesis Program. She applies artificial intelligence (AI) through high-throughput computer approaches to interpret the very large sets of data biomedical research produces.
Q: What’s the role of artificial intelligence in biology?
A: Using AI, we can process big and complex datasets, integrate different data types, extract new knowledge, and identify patterns that describe specific features or outcomes that we are interested in. Using computing power, we try to identify complex patterns in the data to uncover biologically and clinically relevant information and help predict prognosis and treatment response. While AI has deeply transformed other fields, we are not exploiting its full potential in biology yet, but we’re getting there. For example, deep learning methods are currently outperforming any other approach to predicting protein structure.
Q: What attracted you to AI and machine learning?
A: I double majored in computer science and biology because I liked both disciplines. During my Ph.D. training in computer science, I specialized in computational biology and I realized that almost any biologic question could be tackled through AI and that computing power can help analyze very complex data sets.
Q: What are your research interests and goals?
A: I’m interested in how cancer evolves during disease progression. Over time, cancer cells accumulate mutations and other features that favor
their continued growth and survival and eventually promote metastasis. Cells carrying the most favorable alterations are selected, leading to the emergence of different populations. Deciphering this process has important implications for therapy response and resistance to treatment. I also study the evolution of viral infections. I approach these topics using artificial intelligence and develop new software and algorithms to answer different biologic questions.
Q: How will your research and expertise fit into Wistar’s research programs?
A: My research is very collaborative by nature: My lab won’t generate primary data from lab experiments. Instead, we will apply our computational expertise working with other teams to enhance the potential of their data. I see many promising opportunities for collaboration, for example with the Herlyn lab on immunotherapy in melanoma and the Zhang lab on ovarian cancer. I’m excited to join such a dynamic and collaborative environment.
Q: How will your work with scientists support what they do?
A: I think that AI can be useful for many ongoing research projects at Wistar. It can be used, for instance, to identify biomarkers, develop clinical predictors, and uncover deleterious alterations. We can utilize these tools to identify candidate genes or mutations that confer treatment sensitivity or resistance and to predict genomic features that enhance viral pathogenicity and infectivity. Therefore, with the expansive growth of genomic, molecular, and clinical data, machine and deep learning methods offer unique opportunities for biomedical research.
Q: Tell us a little about where you came from, your background, and your interests.
A: I grew up in Israel, where military service is mandatory for everyone, so it’s really nothing special. I served in the Intelligence Corps of the IDF between 2008-2010. My unit was in the School of Intelligence, and we worked on computer-based training projects, where I was team leader of computer graphics from 2009.
I absolutely love research. It’s so much fun that I consider my work also a hobby. I also enjoy sports: long distance running, skiing and snowboarding. Growing up in Israel you’re not exactly close to the mountains, but I used to go skiing in the Alps and loved it. I’ve found many great places in the U.S. too.