Ricardo Henao

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
  • EGR 190: Special Topics in Engineering
  • EGR 393: Research Projects in Engineering
  • EGR 590: Special Topics in Engineering

In the News

Representative Publications

  • Eckhoff, AM; Connor, AA; Thacker, JKM; Blazer, DG; Moore, HG; Scheri, RP; Lagoo-Deenadayalan, SA; Harpole, DH; Seymour, KA; Purves, JT; Ravindra, KV; Southerland, KW; Rocke, DJ; Gilner, JB; Parker, DC; Bain, JR; Muehlbauer, MJ; Ilkayeva, OR; Corcoran, DL; Modliszewski, JL; Devos, N; Foster, MW; Moseley, MA; Dressman, HK; Chan, C; Huebner, JL; Chasse, S; Stempora, L; Aschenbrenner, ME; Joshi, M-B; Hollister, B; Henao, R; Barfield, RT; Ellison, MA; Bailey, S; Woody, S; Huang, ES; Kirk, A; Hwang, ES, A Multidimensional Bioinformatic Platform for the Study of Human Response to Surgery., Ann Surg, vol 275 no. 6 (2022), pp. 1094-1102 [10.1097/SLA.0000000000005429] [abs].
  • Park, C; Jeong, HK; Henao, R; Kheterpal, M, Current Landscape of Generative Adversarial Networks for Facial Deidentification in Dermatology: Systematic Review and Evaluation, Jmir Dermatology, vol 5 no. 2 (2022) [10.2196/35497] [abs].
  • Draelos, RL; Ezekian, JE; Zhuang, F; Moya-Mendez, ME; Zhang, Z; Rosamilia, MB; Manivannan, PKR; Henao, R; Landstrom, AP, GENESIS: Gene-Specific Machine Learning Models for Variants of Uncertain Significance Found in Catecholaminergic Polymorphic Ventricular Tachycardia and Long QT Syndrome-Associated Genes., Circulation. Arrhythmia and Electrophysiology, vol 15 no. 4 (2022) [10.1161/circep.121.010326] [abs].
  • Wisely, CE; Wang, D; Henao, R; Grewal, DS; Thompson, AC; Robbins, CB; Yoon, SP; Soundararajan, S; Polascik, BW; Burke, JR; Liu, A; Carin, L; Fekrat, S, Convolutional neural network to identify symptomatic Alzheimer's disease using multimodal retinal imaging., British Journal of Ophthalmology, vol 106 no. 3 (2022), pp. 388-395 [10.1136/bjophthalmol-2020-317659] [abs].
  • Yang, T; Henao, R, TAMC: A deep-learning approach to predict motif-centric transcriptional factor binding activity based on ATAC-seq profile (2022) [10.1101/2022.02.15.480482] [abs].