Hao Harry Feng, PhD

Assistant Professor
Department of Population and Quantitative Health Sciences
School of Medicine
Member
Cancer Prevention Control & Population Research Program
Case Comprehensive Cancer Center

Dr. Hao Feng develops and applies biostatistical and bioinformatics approaches to better understand high-throughput omics data. He utilizes epigenetic data to define biomarkers, predict and classify disease subtypes. His approaches has wide applications in cancers and neurological studies, such as liver cancer, Alzheimer’s Disease (AD) and PTSD. He also has collaborated in researching how Zika virus alters transcriptome, and how environmental radiation exposure induces epigenetic variables that inform lung cancer. 

Working in industry and academic medicine, he has developed Bayesian methods to study tumor growth inhibition and has developed statistical models for the analysis of genetics data, with applications to cancer immunology and trauma research.

Personal Web page:

https://hfenglab.org

Teaching Information

Courses Taught

PQHS 471 - Machine Learning and Data Mining

Research Information

Research Interests

My research interests include biostatistics, bioinformatics and computational biology. My main focus is to develop and apply biostatistics and bioinformatics methods to better understand high-throughput omics data, with an emphasis on applications in cancer. I utilize epigenetic data to define biomarkers, classify cancer subtypes and predict disease in cell-free DNA. I developed a number of open-source software tools that are freely available on Bioconductor and R-CRAN, with over 10,000 downloads annually. These tools have been widely used in studies on various cancer types. I collaborate closely with physicians and wet-lab researchers to decipher signals from cancer genomics and epigenomics data.

Professional Memberships

American Statistical Association
International Biometric Society – ENAR
International Chinese Statistical Association
American Society of Human Genetics

Publications

Publishing impact 

Metrics from Web of Science/publons and/or Scopus/SciVal: 

  • H-index: 7
  • Total publications from CV: 14 
  • Total citations:  479
  • Publications in top-tier journals: 80% 
  • Collaborative publishing - international: 80%/20%

Editorial roles

Reviewer for publications including:

  • Journal of Applied Statistics
  • Statistical Methods in Medical Research
  • Scientific Reports
  • Journal of Alzheimer’s Disease
  • Aging

View Feng's Publications

Education

Ph.D.
Biostatistics
Emory University
2019
M.S.
Biostatistics
Emory University
2018
MSPH
Biostatistics
Emory University
2013
B.S.
Biosciences
University of Science and Technology of China
2011

Additional Information

Contributions to science:

  • Statistical methods in bioinformatics and computational biology
  • Experimental design, data analysis and method development in high-throughput omics data
  • Biomarker discovery and signal deconvolution in epigenetics data
  • Applications of statistical methods in cancer, neurodegenerative disease, PTSD and cell-free DNA data

Software Developed/Co-Developed:

  • DSS: Dispersion Shrinkage for Sequencing, an R/Biconductor package for differential analysis from high-throughput sequencing data, including differential expression for RNA-seq and differential methylation for bisulfite-sequencing data
  • InfiniumPurify: a comprehensive R package for estimating and accounting for tumor purity based on DNA methylation Infinium 450k array data
  • cfDNAMethy: a reference-free and reference-based method for disease prediction by cell-free DNA methylation

View Feng's personal Website