  Cancer is responsible for millions of deaths worldwide every year. Although
significant progress has been achieved in cancer medicine, many issues remain
to be addressed for improving cancer therapy. Appropriate cancer patient
stratification is the prerequisite for selecting appropriate treatment plan, as
cancer patients are of known heterogeneous genetic make-ups and phenotypic
differences. In this study, built upon deep phenotypic characterizations
extractable from Mayo Clinic electronic health records (EHRs) and genetic test
reports for a collection of cancer patients, we developed a system leveraging a
joint of phenotypic and genetic features for cancer patient subgrouping.
  The workflow is roughly divided into three parts: feature preprocessing,
cancer patient classification, and cancer patient clustering based. In feature
preprocessing step, we performed filtering, retaining the most relevant
features. In cancer patient classification, we utilized joint categorical
features to build a patient-feature matrix and applied nine different machine
learning models, Random Forests (RF), Decision Tree (DT), Support Vector
Machine (SVM), Naive Bayes (NB), Logistic Regression (LR), Multilayer
Perceptron (MLP), Gradient Boosting (GB), Convolutional Neural Network (CNN),
and Feedforward Neural Network (FNN), for classification purposes. Finally, in
the cancer patient clustering step, we leveraged joint embeddings features and
patient-feature associations to build an undirected feature graph and then
trained the cancer feature node embeddings.
