Ricardo Henao

Associate Professor in Biostatistics & Bioinformatics

Appointments and Affiliations

  • Associate Professor in Biostatistics & Bioinformatics
  • Assistant Professor in the Department of Electrical and Computer Engineering
  • Member of Duke Center for Applied Genomics and Precision Medicine
  • Member of the Duke Clinical Research Institute

Contact Information

  • Office Location: 140 Science Drive, Durham, NC 27710
  • Office Phone: (919) 668-0647
  • Email Address: ricardo.henao@duke.edu

Education

  • Ph.D. Technical University of Denmark (Denmark), 2011

Courses Taught

  • ECE 392: Projects in Electrical and Computer Engineering
  • ECE 493: Projects in Electrical and Computer Engineering
  • ECE 494: Projects in Electrical and Computer Engineering
  • ECE 891: Internship
  • ECE 899: Special Readings in Electrical Engineering
  • EGR 190: Special Topics in Engineering
  • EGR 393: Research Projects in Engineering
  • EGR 590: Special Topics in Engineering
  • IDS 793: Independent Study

In the News

Representative Publications

  • Moris, Dimitrios, Ricardo Henao, Hannah Hensman, Linda Stempora, Scott Chasse, Seth Schobel, Christopher J. Dente, Allan D. Kirk, and Eric Elster. “Multidimensional machine learning models predicting outcomes after trauma.” Surgery, September 15, 2022. https://doi.org/10.1016/j.surg.2022.08.007.
  • Yang, Tianqi, and Ricardo Henao. “TAMC: A deep-learning approach to predict motif-centric transcriptional factor binding activity based on ATAC-seq profile.” Plos Comput Biol 18, no. 9 (September 12, 2022): e1009921. https://doi.org/10.1371/journal.pcbi.1009921.
  • Eckhoff, Austin M., Ashton A. Connor, Julie K. M. Thacker, Dan G. Blazer, Harvey G. Moore, Randall P. Scheri, Sandhya A. Lagoo-Deenadayalan, et al. “A Multidimensional Bioinformatic Platform for the Study of Human Response to Surgery.” Ann Surg 275, no. 6 (June 1, 2022): 1094–1102. https://doi.org/10.1097/SLA.0000000000005429.
  • Park, C., H. K. Jeong, R. Henao, and M. Kheterpal. “Current Landscape of Generative Adversarial Networks for Facial Deidentification in Dermatology: Systematic Review and Evaluation.” Jmir Dermatology 5, no. 2 (May 1, 2022). https://doi.org/10.2196/35497.
  • Draelos, Rachel L., Jordan E. Ezekian, Farica Zhuang, Mary E. Moya-Mendez, Zhushan Zhang, Michael B. Rosamilia, Perathu K. R. Manivannan, Ricardo Henao, and Andrew P. Landstrom. “GENESIS: Gene-Specific Machine Learning Models for Variants of Uncertain Significance Found in Catecholaminergic Polymorphic Ventricular Tachycardia and Long QT Syndrome-Associated Genes.” Circ Arrhythm Electrophysiol 15, no. 4 (April 2022): e010326. https://doi.org/10.1161/CIRCEP.121.010326.