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Noam Auslander, Ph.D.


The Auslander Lab



Assistant Professor, Molecular & Cellular Oncogenesis Program, Ellen and Ronald Caplan Cancer Center

About the Scientist

Auslander focuses on developing machine learning methods to understand genetic and infectious factors that drive cancer evolution and identify patterns that can improve cancer diagnosis and treatment.

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

In the past years, enormous growth in the quantity of biomedical data has been paralleled by advances in computational and deep learning techniques. These rapidly accumulating biomedical and clinical datasets have the potential to uncover unknown genetic drivers of cancer, carcinogenic infectious agents, and new treatment biomarkers. To achieve this potential, there is a need for improved and more interpretable computational strategies that can handle different types of biomedical data.

The Auslander laboratory develops advanced machine and deep learning methods to identify factors that drive cancer development and predict patients’ prognoses. We focus on the development of deep learning methods to identify new infectious agents in cancer, and the development of machine learning strategies that improve outcome prediction through biologically interpretable classifiers.


Postdoctoral Fellow

Abdurrahman Elbasir 

Graduate Student

Andrew Patterson (UPenn-GCB)

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 cancer infectious agents.

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


Characterization of the Cancer Microbiome by Deep Learning

While the burden of cancer caused by infections is high, the attributable fraction is believed to be highly under-estimated by much of the cancer community. Identification of new cancer microbiomes has the potential to significantly reduce the global impact of cancer. However, identification of viruses or bacteria in tumors is highly challenging with short RNA sequencing reads.

To address this challenge, our lab develops deep learning-based frameworks to help characterize the landscape of microbiomes that are expressed in different cancer types and correlate new cancer-associated microbial species with prognoses and treatment responses.

Biologically Informed Classifiers of Cancer Treatment Responses

A major difficulty limiting the translational potential of machine learning methods is low biological interpretability. More complex models often lead to better performance, but these are typically less interpretable and therefore less likely to contribute to biological or clinical cancer research. 

Our lab develops methods to construct biologically motivated classifiers of cancer treatment responses and prognoses by incorporating biological knowledge and databases with cancer genomics. 

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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