Cloud-scale genomic analysis
Research with Soo-Yeon Ji on processing and classification methods for gene-expression microarray data.
Dr. Benjamin Harvey · Research portfolio
Science in service of people.
Science in service of people.Genomics & Bioinformatics
Computational research in human health.
How can scalable analysis turn complex biological and population data into useful evidence?
The problem.
Genomic, proteomic, and population-health datasets require methods that remain practical at scale and meaningful in context. This part of my research record spans cloud-scale cancer genomics, population-health risk, and proteome-wide association studies.
My research contributions.
Population-health modeling
Coauthored research connecting individual and community context in COVID-19 mortality risk.
Proteomics across ancestry groups
Coauthored Nature Genetics research on cis-pQTLs and models for proteome-wide association studies in individuals of European and African ancestry.
Chronology & work.
An established research foundation with coauthored journal and conference publications. These papers are linked to their original records and retain their publication dates.
2014
Peer-reviewed conference paper
Cloud-scale genomic signals processing classification analysis for gene expression microarray data
Benjamin Harvey and Soo-Yeon Ji.
Contribution & source
Cloud-scale methods for classification of genomic data.
IEEE EMBC, 7152–7155
Read the original source 2017 · online 2015
Peer-reviewed journal article
Cloud-Scale Genomic Signals Processing for Robust Large-Scale Cancer Genomic Microarray Data Analysis
Benjamin Simeon Harvey and Soo-Yeon Ji.
Contribution & source
Scalable cancer genomic microarray data analysis.
IEEE JBHI 21(1)
Read the original source 2021 · online December 2020
Peer-reviewed journal article
Individual and community-level risk for COVID-19 mortality in the United States
Coauthored with Jin and colleagues.
Contribution & source
A framework linking individual and community-level risk.
Nature Medicine 27, 264–269
Read the original source 2022
Peer-reviewed journal article
Plasma proteome analyses in individuals of European and African ancestry identify cis-pQTLs and models for proteome-wide association studies
Coauthored with Zhang and colleagues.
Contribution & source
Genetic and proteomic evidence across ancestry groups.
Nature Genetics 54, 593–602
Read the original source Reading the research.
Read the methods
The original papers provide the study design, populations, and assumptions.
Keep the context
Historical health studies should be interpreted in the settings and periods examined.
Follow the evidence
Publisher and PubMed records anchor the publication chronology.
Sources & collaboration.
This public research profile was prepared from my research portfolio and source register dated September 21, 2026, together with the linked publication records. Dates shown as source-record dates identify the available version; undated work remains undated.
Let’s define the next question.
Research collaborations across national security, civilian institutions, and human health.
Discuss the research