netDx: interpretable patient classification using integrated patient similarity networks.
Mol Syst Biol
; 15(3): e8497, 2019 03 14.
Article
in En
| MEDLINE
| ID: mdl-30872331
ABSTRACT
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.
Key words
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Asthma
/
Algorithms
/
Software
/
Breast Neoplasms
/
Machine Learning
Type of study:
Diagnostic_studies
/
Prognostic_studies
Limits:
Female
/
Humans
Language:
En
Journal:
Mol Syst Biol
Journal subject:
BIOLOGIA MOLECULAR
/
BIOTECNOLOGIA
Year:
2019
Document type:
Article
Affiliation country:
Canada
Publication country:
ENGLAND
/
ESCOCIA
/
GB
/
GREAT BRITAIN
/
INGLATERRA
/
REINO UNIDO
/
SCOTLAND
/
UK
/
UNITED KINGDOM