# BECKON - Block Estimate Chain: creating Knowledge ON demand & protecting privacy

> **NIH NIH R00** · UNIVERSITY OF CALIFORNIA, SAN DIEGO · 2020 · $249,000

## Abstract

7. Project Summary/Abstract
With the wide adoption of electronic health record systems, cross-institutional genomic medicine predictive
modeling is becoming increasingly important, and have the potential to enable generalizable models to
accelerate research and facilitate quality improvement initiatives. For example, understanding whether a
particular variable has clinical significance depends on a variety of factors, one important one being statistically
significant associations between the variant and clinical phenotypes. Multivariate models that predict
predisposition to disease or outcomes after receiving certain therapeutic agents can help propel genomic
medicine into mainstream clinical care. However, most existing privacy-preserving machine learning methods
that have been used to build predictive models given clinical data are based on centralized architecture, which
presents security and robustness vulnerabilities such as single-point-of-failure.
In this proposal, we will develop novel methods for decentralized privacy-preserving genomic medicine predictive
modeling, which can advance comparative effectiveness research, biomedical discovery, and patient-care. Our
first aim is to develop a predictive modeling framework on private Blockchain networks. This aim relies on the
Blockchain technology and consensus protocols, as well as the online and batch machine learning algorithms,
to provide an open-source Blockchain-based privacy-preserving predictive modeling library for further
Blockchain-related studies and applications. We will characterize settings in which Blockchain technology offers
advances over current technologies. The second aim is to develop a Blockchain-based privacy-preserving
genomic medicine modeling architecture for real-world clinical data research networks. These aims are devoted
to the mission of the National Human Genome Research Institute (NHGRI) to develop biomedical technologies
with application domain of genomics and healthcare.
The NIH Pathway to Independence Award provides a great opportunity for the applicant to complement his
computer science background with biomedical knowledge, and specialized training in machine learning and
knowledge-based systems. It will also allow him to investigate new techniques to advance genomic and
healthcare privacy protection. The success of the proposed project will help his long-term career goal of obtaining
a faculty position at a biomedical informatics program at a major US research university and conduct
independently funded research in the field of decentralized privacy-preserving computation.

## Key facts

- **NIH application ID:** 9920181
- **Project number:** 5R00HG009680-04
- **Recipient organization:** UNIVERSITY OF CALIFORNIA, SAN DIEGO
- **Principal Investigator:** Tsung-Ting Kuo
- **Activity code:** R00 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $249,000
- **Award type:** 5
- **Project period:** 2019-05-01 → 2022-04-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9920181

## Citation

> US National Institutes of Health, RePORTER application 9920181, BECKON - Block Estimate Chain: creating Knowledge ON demand & protecting privacy (5R00HG009680-04). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/9920181. Licensed CC0.

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