Papers

  1. David Koes, Jonathan King, Paul Francoeur, et al. “DREAMing of big data and scalable machine learning: Predicting kinase binding with matrix factorization”. Abstracts of Papers of the American Chemical Society (2019).

  2. Jocelyn Sunseri, Jonathan King, Paul Francoeur, et al. “Convolutional neural network scoring
and minimization in the D3R 2017 community challenge”. Journal of Computer-Aided Molecular Design (2018). Source.

  3. Ran Xiao, Jonathan King, Andrea Villaroman, Duc H. Do, Noel G. Boyle and Xiao Hu, Senior Member, IEEE. “Predict In-Hospital Code Blue Events using Monitor Alarms through Deep Learning Approach”. IEEE Engineering in Medicine and Biology Society Proceedings (2018). Source.

Presentations

  1. Jonathan King. “Exploring sequence-to-sequence learning methods for end-to-end, complete protein structure prediction”
    • American Chemical Society National Conference, Computational Chemistry Division (2019).
    • Canadian Chemistry Conference, Machine Learning Division (2019).
    • Canadian Chemistry Conference, Machine Learning Division (2019).

  2. Jonathan King. “A Novel Algorithm for Detecting FLT3 Internal Tandem Duplications in Patients With Acute Myeloid Leukemia”. Best Presentation. Northern California Computational Biology Student Symposium (2016). Source.

  3. Jackelyne Garcia Cruz, Jonathan King. “Screening and Simulating Potential Inhibitors for the CYP4F2 Enzyme”.
    • Summer Undergraduate Research Symposium, Duquesne University (2019).
    • Annual Biomedical Research Conference for Minority Students (2019).
  4. Alex Ludwig, Jonathan King. “Developing a Latent Space Representation for Prediction of both RNA Terminator Strength and Structure”. Summer Undergraduate Research Symposium, Duquesne University (2018).