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Biomedical Informatics

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Chief Informatics Officer, Executive Director of Biomedical Informatics:
David J. Foran, PhD

Cancer research continues to become increasingly data-driven and many investigative studies underway at Rutgers Cancer Institute of New Jersey rely upon analysis of multi-dimensional data sets, high-resolution imaging, next generation sequencing and other information-intensive technologies. The Biomedical Informatics shared resource (Bioinformatics) addresses these challenges through the use of high-throughput instrumentation, advanced data management systems, machine-learning technologies, high-performance cloud computing environments and state-of-the-art supercomputing capabilities.

The overarching mission of Bioinformatics is to provide leading-edge data acquisition and analysis tools, computational informatics expertise, data analysis, and intensive training to foster advances in research and discovery in investigative oncology.  Application of these activities to genomic data from patient samples is enhancing patient care and initiating and sustaining productive collaborations among Rutgers Cancer Institute investigators and throughout the clinical and basic research community.

To optimize the support we provide to our basic, clinical and population research programs, Bioinformatics is organized into the following sections:

  • Computational Imaging
  • Clinical and Research Information Technology (IT)
  • Chemical Informatics and Drug Discovery
  • Bioinformatics and Systems Biology.

Under the leadership of Dr. David J. Foran, the team’s deep background and experience provide Rutgers Cancer Institute members with innovative services and the ability to:

  • Design and perform next generation sequencing, analysis, and screening to identify putative cancer driver genes, classify tumors and identify oncogenes, tumor suppressors, and genetic pathways;
  • Design, develop and optimize high-throughput processing and modeling in large, high-dimensional, multi-modal biomedical data sets including combinations of digitized microscopy, radiologic imaging studies, genomics and clinical correlates through the use of cloud and supercomputing resources;
  • Automate extraction, mapping, warehousing and mining of data originating from distributed data sources including EMRs, clinical trials management systems, next generation sequencing devices and imaging (pathology and radiology) archives;
  • Leverage advanced multispectral imaging, color-decomposition, spectral classification and antibody/ antigen, quantum dot conjugate technology to facilitate whole-slide, fluorescent, and multi-spectral digital microscopy services;
  • Develop and optimize computational pathology and radiology imaging and cutting-edge decision support algorithms, methods and strategies to guide choices in treatment and therapy planning, while improving diagnostic and prognostic accuracy and personalizing the management of patients under our care;
  • Provide expert consultation to support molecular dynamics analyses and in silico screening studies to identify lead targets for design and development of novel therapeutics and facilitate drug discovery; and
  • Organize and provide support for advanced training, national workshops, and educational programs.

By providing these services, the shared resource will continue to provide insight into the underlying mechanisms of disease onset and progression while facilitating advances in cancer detection, diagnosis, prognosis, and treatment. 

 

PUBLICATION ACKNOWLEDGEMENT 

"Services, results and/or products in support of the research project were generated by the Rutgers Cancer Institute of New Jersey Biomedical Informatics Shared Resource, supported, in part, with funding from NCI-CCSG P30CA072720-5917."

Please consider including the names of individuals from the shared resource if they provided any intellectual input or additional effort.

NIH Public Access Policy: Publications that result from services provided by this Shared Resource must be compliant with the NIH Public Access policy by submitting your paper to PubMed Central. Go to: https://publicaccess.nih.gov/submit_process.htm for PubMed Central's submission methods instructions.  

If you require additional guidance on how to properly acknowledge a single shared resource or multiple shared resources you may contact Janet Bandoy, Shared Resource Coordinator.

 

 

 

 

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