After processing the data, we learn the Hierarchical Interaction Network (HINT) on the following four tasks. The following figure illustrates the pipeline of HINT. All the scripts are available in the folder HINT
.
tutorial_HINT.ipynb
is a tutorial to learn and evaluate HINT step by step.
Phase-level prediction predicts the success probability of a single phase study.
python HINT/learn_phaseI.py
python HINT/learn_phaseII.py
python HINT/learn_phaseIII.py
Please contact futianfan@gmail.com for help or submit an issue. This is a joint work with Kexin Huang, Cao(Danica) Xiao, Lucas M. Glass and Jimeng Sun.
learn_phaseI.py
: predict whether the trial can pass phase I. learn_phaseII.py
: predict whether the trial can pass phase II.learn_phaseIII.py
: predict whether the trial can pass phase III.learn_indication.py
: predict whether the trial can pass the indication (phase I-III).model.py
Interaction
, HINT_nograph
, HINTModel
), build model from simple to complex. icdcode_encode.py
molecule_encode.py
protocol_encode.py
module.py
contains standard implementation of existing neural module, e.g., highway, GCN