Core 1 - Research &Development Contemporary biomedical and behavioral sciences require sophisticated computation. In Core 1, a team of quantitative scientists (information and computer scientists, biostatisticians, mathematicians, and software engineers) will develop the software infrastructure (i.e. the BCl core), services, and tools for use by biomedical and behavioral researchers. An illustration of major components is shown in Figure B-1. Current state of the art research infrastructures containing biomedical data warehouses essentially have three levels of data disclosure: (1) query results counts, (2) de-identified data, and (3) identified data. Deidentification and anonymization are related, but different concepts. While de-identification consists of removal of particular identifiers, anonymization provides a means for data not be traced back to one particular individual. Simplistic measures (Murphy SN &Chueh HC 2002) are cun-ently applied to step (1) above to prevent the tracing of information to a particular individual using the results of several query counts, and previous research indicates that the de-identification of data disclosed at level (2) is not sufficient to preserve individual privacy (Sweeney 1997). Therefore, at both levels (1) and (2) robust anonymization algorithms are necessary. Formal proofs for adherence to quantitative privacy criteria are hard to produce, and consequently only available for a few methods in limited settings (Lasko 2007). As a consequence, most approaches in use today have not been rigorously validated theoretically or with real data. The three levels of disclosure outlined above are insufficient for responsible data sharing beyond the scope of an institutional IRB (in a HIPAA covered entity) such as a federated data warehouse to which multiple institutions or sources can contribute data. For this and other reasons, institutional clinical data repositories for research, some of which receive federal funding for their creation and/or maintenance, have been restricted to researchers who are formally affiliated with the institution. To address this limitation and progress towards a stage in which data can be shared across institutions, we propose research into: (a) a tool that interfaces between clinical data and a user, and that can answer limited queries while ensuring that privacy is preserved, (b) a tool that can simulate real data in a privacy preserving manner to the point that the simulated data can be used as a proxy in population based analyses, and (c) a cryptographic data submission protocol that hides the identity of the submitting entity.

Agency
National Institute of Health (NIH)
Institute
National Heart, Lung, and Blood Institute (NHLBI)
Type
Specialized Center--Cooperative Agreements (U54)
Project #
5U54HL108460-05
Application #
8697118
Study Section
Special Emphasis Panel (ZRG1)
Project Start
Project End
Budget Start
2014-07-01
Budget End
2015-06-30
Support Year
5
Fiscal Year
2014
Total Cost
Indirect Cost
Name
University of California San Diego
Department
Type
DUNS #
City
La Jolla
State
CA
Country
United States
Zip Code
92093
Wang, Lichang; Fang, Yong; Aref, Dima et al. (2016) PALME: PAtients Like My gEnome. AMIA Jt Summits Transl Sci Proc 2016:219-24
Levy, Eric; Marty, Rachel; Gárate Calderón, Valentina et al. (2016) Immune DNA signature of T-cell infiltration in breast tumor exomes. Sci Rep 6:30064
Wang, Shuang; Zhang, Yuchen; Dai, Wenrui et al. (2016) HEALER: homomorphic computation of ExAct Logistic rEgRession for secure rare disease variants analysis in GWAS. Bioinformatics 32:211-8
Wang, Shuang; Jiang, Xiaoqian; Singh, Siddharth et al. (2016) Genome privacy: challenges, technical approaches to mitigate risk, and ethical considerations in the United States. Ann N Y Acad Sci :
Shi, Eileen; Chmielecki, Juliann; Tang, Chih-Min et al. (2016) FGFR1 and NTRK3 actionable alterations in ""Wild-Type"" gastrointestinal stromal tumors. J Transl Med 14:339
Farhan, Wael; Wang, Zhimu; Huang, Yingxiang et al. (2016) A Predictive Model for Medical Events Based on Contextual Embedding of Temporal Sequences. JMIR Med Inform 4:e39
Tang, Haixu; Jiang, Xiaoqian; Wang, Xiaofeng et al. (2016) Protecting genomic data analytics in the cloud: state of the art and opportunities. BMC Med Genomics 9:63
Azad, Priti; Zhao, Huiwen W; Cabrales, Pedro J et al. (2016) Senp1 drives hypoxia-induced polycythemia via GATA1 and Bcl-xL in subjects with Monge's disease. J Exp Med 213:2729-2744
Doan, Son; Maehara, Cleo K; Chaparro, Juan D et al. (2016) Building a Natural Language Processing Tool to Identify Patients With High Clinical Suspicion for Kawasaki Disease from Emergency Department Notes. Acad Emerg Med 23:628-36
Shi, Haoyi; Jiang, Chao; Dai, Wenrui et al. (2016) Secure Multi-pArty Computation Grid LOgistic REgression (SMAC-GLORE). BMC Med Inform Decis Mak 16 Suppl 3:89

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