SUBSTRA: Supervised Bayesian Patient Stratification.
Bioinformatics
; 35(18): 3263-3272, 2019 09 15.
Article
em En
| MEDLINE
| ID: mdl-30768166
ABSTRACT
MOTIVATION Patient stratification methods are key to the vision of precision medicine. Here, we consider transcriptional data to segment the patient population into subsets relevant to a given phenotype. Whereas most existing patient stratification methods focus either on predictive performance or interpretable features, we developed a method striking a balance between these two important goals. RESULTS:
We introduce a Bayesian method called SUBSTRA that uses regularized biclustering to identify patient subtypes and interpretable subtype-specific transcript clusters. The method iteratively re-weights feature importance to optimize phenotype prediction performance by producing more phenotype-relevant patient subtypes. We investigate the performance of SUBSTRA in finding relevant features using simulated data and successfully benchmark it against state-of-the-art unsupervised stratification methods and supervised alternatives. Moreover, SUBSTRA achieves predictive performance competitive with the supervised benchmark methods and provides interpretable transcriptional features in diverse biological settings, such as drug response prediction, cancer diagnosis, or kidney transplant rejection. AVAILABILITY AND IMPLEMENTATION The R code of SUBSTRA is available at https//github.com/sahandk/SUBSTRA. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Software
Tipo de estudo:
Prognostic_studies
Idioma:
En
Ano de publicação:
2019
Tipo de documento:
Article