netdx interpretable patient classification using integrated patient similarity networks
Copyright policy )netdx interpretable patient classification using integrated patient similarity networks
AbstractPatient classification has widespread biomedical and clinical applications, including diagnosis, prognosis and treatment response prediction. A clinically useful prediction algorithm should be accurate, generalizable, be able to integrate diverse data types, and handle sparse data. A clinical predictor based on genomic data needs to be easily interpretable to drive hypothesis-driven research into new treatments. We describe netDx, a novel supervised patient classification framework based on patient similarity networks. netDx meets the above criteria and particularly excels at data integration and model interpretability. As a machine learning method, netDx demonstrates consistently excellent performance in a cancer survival benchmark across four cancer types by integrating up to six genomic and clinical data types. In these tests, netDx has significantly higher average performance than most other machine-learning approaches across most cancer types and its best model outperforms all other methods for two cancer types. In comparison to traditional machine learning-based patient classifiers, netDx results are more interpretable, visualizing the decision boundary in the context of patient similarity space. When patient similarity is defined by pathway-level gene expression, netDx identifies biological pathways important for outcome prediction, as demonstrated in diverse data sets of breast cancer and asthma. Thus, netDx can serve both as a patient classifier and as a tool for discovery of biological features characteristic of disease. We provide a software complete implementation of netDx along with sample files and automation workflows in R.
Medicine (General), QH301-705.5, precision medicine, Breast Neoplasms, Workflow, patient similarity networks, Machine Learning, R5-920, multi‐omics, multimodal data integration, Humans, supervised machine learning, Biology (General), Articles, Genomics, Prognosis, Survival Analysis, Asthma, Benchmarking, Female, Algorithms, Software
Medicine (General), QH301-705.5, precision medicine, Breast Neoplasms, Workflow, patient similarity networks, Machine Learning, R5-920, multi‐omics, multimodal data integration, Humans, supervised machine learning, Biology (General), Articles, Genomics, Prognosis, Survival Analysis, Asthma, Benchmarking, Female, Algorithms, Software
citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).73 popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.Top 1% influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).Top 10% impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.Average
Citations provided by BIP!Popularity provided by BIP!