Mark Gerstein
Yale University, CT·Computer Science
Data are most useful when shared, but sharing sensitive data, such as a person's genome, risks exposing private information. These grants develop ways to measure and limit that risk.
- Fields
Computer Science
Genetics
- Topics
genomic privacy
functional genomics
data sharing
- Grants
- 2 known grants, through Aug 2027
What they work on
- Develop statistical, information-theoretic measures of private information leaked from functional genomics data, testing linking attacks built from variants called from raw reads, signal profiles, Hi-C matrices and expression matrices.
- Create privacy-preserving file formats for sequence alignment maps, signal tracks, 3D genome interaction matrices and multi-individual gene expression matrices.
- Test whether microbiome reads found in human functional genomics datasets reveal private traits and increase re-identification accuracy, using data from ENCODE, TCGA, 1,000 Genomes, gEUVADIS and GTEx.
- Assess the commercial potential of quantum composite states, privacy-preserving quantum objects that summarize a data group, for secure delegated and collaborative quantum computing, through industry interviews.
More about the research
One focuses on genomic data shared between researchers. The other focuses on data sent to remote quantum computers.
Why it matters
Measuring how much private information leaks from functional genomics files, and providing safer file formats, could let large datasets be shared more openly while protecting research participants. A privacy layer for cloud quantum computing could let healthcare, life science, financial, defense and manufacturing organizations use remote quantum hardware without exposing sensitive data.
Known grants
From NIH and NSF research grants. Labs often have other funding we can't see.
Closest labs
Matthew Bailey
Brigham Young University, UT·Genetics
Identify and assess how known cancer predisposition (germline) mutations from curated databases appear in the All of Us whole-genome cohort, and quantify their impact on cancer occurrence.
Thierry Chekouo Tekougang
University of Minnesota, MN·Mathematics & Statistics
Develop Bayesian statistical learning models that capture both linear and nonlinear relationships across genomic, epigenomic, transcriptomic and proteomic measurements so several omics layers can be analyzed jointly.
Rajiv McCoy
Johns Hopkins University, MD·Genetics
Measure how gene expression and splicing vary within and between human populations, using RNA-seq data generated from diverse 1000 Genomes Project samples, and link the patterns to demographic history and natural selection.
The school
Yale University: 73 labs on PI Pages, $1.5B a year on research, 438 research doctorates a year.
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