SUBSTRA: Supervised Bayesian Patient Stratification.
Bioinformatics
; 35(18): 3263-3272, 2019 09 15.
Article
in 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.
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Software
Type of study:
Prognostic_studies
Language:
En
Journal:
Bioinformatics
Journal subject:
INFORMATICA MEDICA
Year:
2019
Document type:
Article
Affiliation country:
Canada