netDx: interpretable patient classification using integrated patient similarity networks

Patient 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 predi...

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Vydané v:	Molecular systems biology Ročník 15; číslo 3; s. e8497 - n/a
Hlavní autori:	Pai, Shraddha, Hui, Shirley, Isserlin, Ruth, Shah, Muhammad A, Kaka, Hussam, Bader, Gary D
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	London Nature Publishing Group UK 01.03.2019 EMBO Press John Wiley and Sons Inc Springer Nature
Predmet:	Age Algorithms Asthma Asthma - classification Asthma - diagnosis Asthma - genetics Automation Benchmarking Breast cancer Breast Neoplasms - classification Breast Neoplasms - diagnosis Breast Neoplasms - genetics Classification Classifiers EMBO10 EMBO17 EMBO42 Female Gene expression Genomes Genomics Humans Learning Learning algorithms Machine Learning multimodal data integration multi‐omics patient similarity networks Patients Precision medicine Prognosis Queries Recommender systems Similarity Similarity measures Software supervised machine learning Survival Analysis Therapeutic applications Workflow supervised machine learning patient similarity networks precision medicine multimodal data integration multi‐omics
ISSN:	1744-4292, 1744-4292
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Popis
Shrnutí:	Patient 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 interpretable to drive hypothesis‐driven research into new treatments. We describe netDx, a novel supervised patient classification framework based on patient similarity networks, which meets these criteria. In a cancer survival benchmark dataset integrating up to six data types in four cancer types, netDx significantly outperforms most other machine‐learning approaches across most cancer types. Compared 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 breast cancer and asthma. netDx can serve as a patient classifier and as a tool for discovery of biological features characteristic of disease. We provide a free software implementation of netDx with automation workflows. Synopsis netDx is a supervised patient classification algorithm based on the paradigm of patient similarity networks. It integrates multi‐omic data and uses biological pathway information to help with model interpretability. In a cancer survival prediction benchmark, netDx performs competitively or better than a diverse panel of machine‐learning algorithms. When patient similarity is defined by pathway‐level gene expression, netDx identifies biological pathways predictive of outcome, as demonstrated in diverse data sets (breast cancer and asthma). netDx is freely available as an R package and as a Docker image. Code, tutorials and worked examples are available at: http://netdx.org . Graphical Abstract netDx is a supervised patient classification algorithm based on the paradigm of patient similarity networks. It integrates multi‐omic data and uses biological pathway information to help with model interpretability.
Bibliografia:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23
ISSN:	1744-4292 1744-4292
DOI:	10.15252/msb.20188497