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Noam Auslander, Ph.D.


The Auslander Lab



Assistant Professor, Molecular & Cellular Oncogenesis Program, The Wistar Institute Cancer Center

About the Scientist

Auslander focuses on developing machine learning methods to understand cancer evolution and unveil vulnerabilities that can be therapeutically targeted. 

Auslander earned her B.S. in computer science and biology from Tel Aviv University and continued her studies in Maryland, where she obtained a computer science Ph.D. from the University of Maryland with a combined fellowship at the National Cancer Institute. She received postdoctoral training at the National Center of Biotechnology Information (NCBI) and joined The Wistar Institute in 2021 as an assistant professor. 

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The Auslander Lab

Cancer evolves through the accumulation of genetic alterations over time. A better understanding of the interplay between different genetic alterations and their contributions to tumor evolution has the potential to reveal differences in therapeutic vulnerabilities, shed light on the temporal patterns and routes of cancer development and provide insights into the biology of tumorigenesis. 

The Auslander laboratory develops advanced computational techniques for the representation and interpretation of cancer omics data, to uncover clinically relevant alterations that promote tumor development and predict patients’ prognosis and treatment response. Specifically, we develop machine and deep learning methods to study cancer evolution and identify markers of treatment resistance and sensitivity. The lab also studies the evolution of pathogenic and oncogenic viruses and develops methods to identify and characterize viral features that confer pathogenic traits. 


Postdoctoral Fellow

Abdurrahman Elbasir 

Research Assistant

Ying Ye

Available Positions

Postdoctoral fellow positions are available in the Auslander laboratory with a research focus on developing machine learning and computational methods to (1) study the evolution of cancer metastases, or (2) identify new oncogenic viruses from tumor data. 

Candidates should have recently received, or be close to obtaining their Ph.D. degree (or equivalent) and have a strong background in one or more of the following disciplines: computer science, data science and machine learning or bioinformatics and computational biology. 

Interested applicants are invited to email


We are interested in using computational techniques to understand how tumors evolve through unique interactions between different types of genetic and epigenetic alterations. Our lab works on methods to integrate different biomedical data types and methods combining machine learning with bioinformatics techniques, to enhance the predictive capacity and interpretability of computational approaches. Using these tools, our lab explores genomic patterns that characterizes tumor progression and harnesses these patterns to generate prognostic markers.  

In addition, we work to integrate machine and deep learning methods with molecular evolution techniques to study the evolution of pathogenic viruses and find new viral elements that are associated with cancer and other diseases. 

Selected Publications

Auslander, N., Gussow, A.B., Benler, S., Wolf, Y.I., Koonin, E.V. “Seeker: alignment-free identification of bacteriophage genomes by deep learning.” Nucleic Acids Res. 2020 Dec 2;48(21):e121. doi: 10.1093/nar/gkaa856.

Gussow, A.B., Auslander, N., Faure, G., Wolf, Y.I., Zhang, F., Koonin, E.V. “Genomic determinants of pathogenicity in SARS-CoV-2 and other human coronaviruses.” Proc Natl Acad Sci U S A. 2020 Jun 30;117(26):15193-15199. doi: 10.1073/pnas.2008176117. Epub 2020 Jun 10.

Auslander, N., Wolf, Y.I., Koonin, E.V. “Interplay between DNA damage repair and apoptosis shapes cancer evolution through aneuploidy and microsatellite instability.” Nat Commun. 2020 Mar 6;11(1):1234. doi: 10.1038/s41467-020-15094-2.

Auslander, N., Wolf, Y.I., Koonin, E.V. “In silico learning of tumor evolution through mutational time series.” Proc Natl Acad Sci U S A. 2019 May 7;116(19):9501-9510. doi: 10.1073/pnas.1901695116.

Auslander, N., Zhang, G., Lee, J.S., Frederick, D.T., Miao, B., Moll, T., Tian, T., Wei, Z., Madan, S., Sullivan, R.J., et al. “Robust prediction of response to immune checkpoint blockade therapy in metastatic melanoma.” Nat Med. 2018 Oct;24(10):1545-1549. doi: 10.1038/s41591-018-0157-9. Epub 2018 Aug 20.

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