In the Englander Institute for Precision Medicine (EIPM) we support both basic and translational research by designing, optimizing, and running high-throughput assays. To this effort, we employ scientists from various disciplines and we use machine learning, AI, and spatial computing to better grasp the dynamic relationships amongst genes and to develop predictive models to identify factors within cell regulatory networks. With these novel insights into disease pathways, we can develop disease models to learn their mechanisms, target effective drug combinations, and sequentially develop new therapeutic approaches. Our faculty and staff are distinguished data analysts who specialize in designing computational methods for extracting insights from next-generation sequencing and high-throughput screening data. EIPM’s computational lab is equipped with state of the art instrumentation, robotics, and customized software that employs numerous software applications to provide high-level analytics and valuable data. Performing these types of analyses can often require extensive computing power and our instruments for conducting them include an array of algorithms and software that were developed by our investigators. Our bioinformatics specialists contribute both in study design and in downstream data analysis. Our main mission is clear: to make precision medicine a reality for every patient through genomics and other clinical data, by pushing the limits of genomics analysis. Our computational team leads an incredible effort to develop tools to further the collection and processing of tumor DNA and the analysis of its genomics information. In addition, we focus on integrating medical, clinical, and scientific perspectives through data, interactive applications, and community-building platforms to empower our scientists to apply bold ideas with transformative potential.

• Computational method uncovers new potential therapeutic option for difficult-to-treat subtype of prostate cancer
• New tool predicts drug targets and IDs new anticancer compounds
• Toxic targets: Saving lives in silico

1. The cancer precision medicine knowledge base for structured clinical-grade mutations and interpretations.
   Huang L, Fernandes H, Zia H, Tavassoli P, Rennert H, Pisapia D, Imielinski M, Sboner A, Rubin MA, Kluk M, Elemento O.
   J Am Med Inform Assoc. 2017 May 1;24(3):513-519. doi: 10.1093/jamia/ocw148.
   PMID: 27789569.
2. A Computational Drug Repositioning Approach for Targeting Oncogenic Transcription Factors.
   Gayvert KM, Dardenne E, Cheung C, Boland MR, Lorberbaum T, Wanjala J, Chen Y, Rubin MA, Tatonetti NP, Rickman DS, Elemento O.
   Cell Rep. 2016 Jun 14;15(11):2348-56. doi: 10.1016/j.celrep.2016.05.037. Epub 2016 Jun 2.
   PMID: 27264179.
3. Practical Analysis of Genome Contact Interaction Experiments.
   Carty MA, Elemento O.
   Methods Mol Biol. 2016;1418:177-89. doi: 10.1007/978-1-4939-3578-9_9.
   PMID: 27008015.
4. A Data-Driven Approach to Predicting Successes and Failures of Clinical Trials.
   Gayvert KM, Madhukar NS, Elemento O.
   Cell Chem Biol. 2016 Oct 20;23(10):1294-1301. doi: 10.1016/j.chembiol.2016.07.023. Epub 2016 Sep 15.
   PMID: 27642066.
5. A primer on precision medicine informatics.
   Sboner A, Elemento O.
   Brief Bioinform. 2016 Jan;17(1):145-53. doi: 10.1093/bib/bbv032. Epub 2015 Jun 5. Review.
   PMID: 26048401.A primer on precision medicine informatics.
6. Development and validation of a whole-exome sequencing test for simultaneous detection of point mutations, indels and copy-number alterations for precision cancer care.
   Rennert H, Eng K, Zhang T, Tan A, Xiang J, Romanel A, Kim R, Tam W, Liu YC, Bhinder B, Cyrta J, Beltran H, Robinson B, Mosquera JM, Fernandes H, Demichelis F, Sboner A, Kluk M, Rubin MA, Elemento O.
   NPJ Genom Med. 2016;1. pii: 16019. doi: 10.1038/npjgenmed.2016.19. Epub 2016 Jul 20.
   PMID: 28781886.