English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Cancer is responsible for millions of deaths worldwide every year. Althoughsignificant progress has been achieved in cancer medicine, many issues remainto be addressed for improving cancer therapy. Appropriate cancer patientstratification is the prerequisite for selecting appropriate treatment plan, ascancer patients are of known heterogeneous genetic make-ups and phenotypicdifferences. In this study, built upon deep phenotypic characterizationsextractable from Mayo Clinic electronic health records (EHRs) and genetic testreports for a collection of cancer patients, we developed a system leveraging ajoint 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 featurepreprocessing step, we performed filtering, retaining the most relevantfeatures. In cancer patient classification, we utilized joint categoricalfeatures to build a patient-feature matrix and applied nine different machinelearning models, Random Forests (RF), Decision Tree (DT), Support VectorMachine (SVM), Naive Bayes (NB), Logistic Regression (LR), MultilayerPerceptron (MLP), Gradient Boosting (GB), Convolutional Neural Network (CNN),and Feedforward Neural Network (FNN), for classification purposes. Finally, inthe cancer patient clustering step, we leveraged joint embeddings features andpatient-feature associations to build an undirected feature graph and thentrained the cancer feature node embeddings.

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