About Us

At the Icahn Institute, our vision is to transform biomedical research and healthcare delivery into a data-driven, evidence-based, patient-tailored discipline. Our research program focuses on harnessing huge molecular datasets to address complex diseases, using advanced biotechnologies to rapidly test novel treatments tailored to each patient, and bringing these treatments to benefit patients faster than has ever been possible.

Meet Our Team

The Institute for Data Science and Genomic Technology is led by a world-class team of clinicians and researchers. Our team members have expertise in a wide range of areas including biophysics and systems pharmacology, cancer biology, stem cell research, immunology, microbiology, and neuroscience, as well as genetics and genomics. We combine backgrounds in pure and applied research.

Adam A. Margolin, PhD

Director of the Icahn Institute for Data Science
and Genomic Technology

Recent Featured Publications

Raj, T., Li, Y.I., Wong, G., Humphrey, J., Wang, M., Ramdhani, S., Wang, Y.-C., Ng, B., Gupta, I., Haroutunian, V., et al. (2018).
Integrative transcriptome analyses of the aging brain implicate altered splicing in Alzheimer’s disease susceptibility.
Nature Genetics.

Lin, L., Chen, Q., Hirsch, J.P., Yoo, S., Yeung, K., Bumgarner, R.E., Tu, Z., Schadt, E.E., and Zhu, J. (2018).
Temporal genetic association and temporal genetic causality methods for dissecting complex networks.
Nature Communications 9, 3980.

Fazlollahi, M., Lee, T.D., Andrade, J., Oguntuyo, K., Chun, Y., Grishina, G., Grishin, A., and Bunyavanich, S. (2018).
The nasal microbiome in asthma.
Journal of Allergy and Clinical Immunology 142, 834-843.e2.

Wang, L., Saci, A., Szabo, P.M., Chasalow, S.D., Castillo-Martin, M., Domingo-Domenech, J., Siefker-Radtke, A., Sharma, P., Sfakianos, J.P., Gong, Y., et al. (2018).
EMT- and stroma-related gene expression and resistance to PD-1 blockade in urothelial cancer.
Nature Communications 9, 3503.

Akers, N.K., Schadt, E.E., and Losic, B. (2018).
STAR Chimeric Post for rapid detection of circular RNA and fusion transcripts.
Bioinformatics 34, 2364–2370.

Lee, E., Collazo-Lorduy, A., Castillo-Martin, M., Gong, Y., Wang, L., Oh, W.K., Galsky, M.D., Cordon-Cardo, C., and Zhu, J. (2018).
Identification of microR-106b as a prognostic biomarker of p53-like bladder cancers by ActMiR.
Oncogene.

Pandey, G., Pandey, O.P., Rogers, A.J., Ahsen, M.E., Hoffman, G.E., Raby, B.A., Weiss, S.T., Schadt, E.E., and Bunyavanich, S. (2018).
A Nasal Brush-based Classifier of Asthma Identified by Machine Learning Analysis of Nasal RNA Sequence Data.
Scientific Reports 8, 8826.

Barbosa, M., Joshi, R.S., Garg, P., Martin-Trujillo, A., Patel, N., Jadhav, B., Watson, C.T., Gibson, W., Chetnik, K., Tessereau, C., et al. (2018).
Identification of rare de novo epigenetic variations in congenital disorders.
Nature Communications 9, 2064.

Chan, Y.-F.Y., Bot, B.M., Zweig, M., Tignor, N., Ma, W., Suver, C., Cedeno, R., Scott, E.R., Gregory Hershman, S., Schadt, E.E., et al. (2018).
The asthma mobile health study, smartphone data collected using ResearchKit.
Scientific Data 5, 180096.

Verbanck, M., Chen, C.-Y., Neale, B., and Do, R. (2018).
Detection of widespread horizontal pleiotropy in causal relationships inferred from Mendelian randomization between complex traits and diseases.
Nature Genetics 50, 693–698.

Zhang, S.-Y., Clark, N.E., Freije, C.A., Pauwels, E., Taggart, A.J., Okada, S., Mandel, H., Garcia, P., Ciancanelli, M.J., Biran, A., et al. (2018).
Inborn Errors of RNA Lariat Metabolism in Humans with Brainstem Viral Infection.
Cell 172, 952-965.e18.

Lempiäinen, H., Brænne, I., Michoel, T., Tragante, V., Vilne, B., Webb, T.R., Kyriakou, T., Eichner, J., Zeng, L., Willenborg, C., et al. (2018).
Network analysis of coronary artery disease risk genes elucidates disease mechanisms and druggable targets.
Scientific Reports 8, 3434.

Hui, K.Y., Fernandez-Hernandez, H., Hu, J., Schaffner, A., Pankratz, N., Hsu, N.-Y., Chuang, L.-S., Carmi, S., Villaverde, N., Li, X., et al. (2018).
Functional variants in the LRRK2 gene confer shared effects on risk for Crohn’s disease and Parkinson’s disease.
Science Translational Medicine 10.

Hoffman, G.E., and Brennand, K.J. (2018).
Mapping regulatory variants in hiPSC models.
Nature Genetics 50, 1–2.

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