Created by W.Langdon from gp-bibliography.bib Revision:1.6970
We introduce two new features implemented in TPOT that helps increase the system's scalability: Feature Set Selector (FSS) and Template. FSS provides the option to specify subsets of the features as separate datasets, assuming the signals come from one or more of these specific data subsets. FSS increases TPOT's efficiency in application on big data by slicing the entire dataset into smaller sets of features and allowing GP to select the best subset in the final pipeline. Template enforces type constraints with strongly typed GP and enables the incorporation of FSS at the beginning of each pipeline. Consequently, FSS and Template help reduce TPOT computation time and may provide more interpretable results. Our simulations show TPOT-FSS significantly outperforms a tuned XGBoost model and standard TPOT implementation. We apply TPOT-FSS to real RNA-Seq data from a study of major depressive disorder. Independent of the previous study that identified significant association with depression severity of two modules, TPOT-FSS corroborates that one of the modules is largely predictive of the clinical diagnosis of each individual.
Detailed simulation and analysis code needed to reproduce the results in this study is available at https://github.com/lelaboratoire/tpot-fss.
Implementation of the new TPOT operators is available at https://github.com/EpistasisLab/tpot. Supplementary data are available at Bioinformatics online.",
PMID: 31165141; PMCID: PMC6956793",
Genetic Programming entries for Trang T Le Weixuan Fu Jason H Moore