Leveraging a Joint of Phenotypic and Genetic Features on Cancer Patient Subgrouping

• URL: http://arxiv.org/abs/2103.16316v1
• Date: Tue, 30 Mar 2021 13:07:05 GMT
• Title: Leveraging a Joint of Phenotypic and Genetic Features on Cancer Patient Subgrouping
• Authors: David Oniani, Chen Wang, Yiqing Zhao, Andrew Wen, Hongfang Liu, Feichen Shen
• Abstract summary: We developed a system leveraging a joint of phenotypic and genetic features for cancer patient subgrouping. In feature preprocessing, we performed filtering, retaining the most relevant features. In cancer patient classification, we utilized 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)
• Score: 7.381190270069632
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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