title: PCM-SABRE: a platform for benchmarking and comparing outcome prediction methods in precision cancer medicine.
abstract: BACKGROUND
  Numerous publications attempt to predict cancer survival outcome from gene expression data using machine-learning methods. A direct comparison of these works is challenging for the following reasons: (1) inconsistent measures used to evaluate the performance of different models, and (2) incomplete specification of critical stages in the process of knowledge discovery. There is a need for a platform that would allow researchers to replicate previous works and to test the impact of changes in the knowledge discovery process on the accuracy of the induced models.
  
  
  RESULTS
  We developed the PCM-SABRE platform, which supports the entire knowledge discovery process for cancer outcome analysis. PCM-SABRE was developed using KNIME. By using PCM-SABRE to reproduce the results of previously published works on breast cancer survival, we define a baseline for evaluating future attempts to predict cancer outcome with machine learning. We used PCM-SABRE to replicate previous work that describe predictive models of breast cancer recurrence, and tested the performance of all possible combinations of feature selection methods and data mining algorithms that was used in either of the works. We reconstructed the work of Chou et al. observing similar trends - superior performance of Probabilistic Neural Network (PNN) and logistic regression (LR) algorithms and inconclusive impact of feature pre-selection with the decision tree algorithm on subsequent analysis.
  
  
  CONCLUSIONS
  PCM-SABRE is a software tool that provides an intuitive environment for rapid development of predictive models in cancer precision medicine.
authors.affiliation: Shraga Segal Department of Microbiology and Immunology, Faculty of Health Sciences, Ben-Gurion University of the Negev, Beer-Sheva, 84105, Israel. eyalnoa@post.bgu.ac.il.
authors.forename: Noah
authors.identifier: http://orcid.org/0000-0001-7098-5106
authors.initials: N
authors.lastname: Eyal-Altman
authors.affiliation: Department of Software and Information Systems Engineering, Ben-Gurion University of the Negev, Beer-Sheva, 84105, Israel.
authors.forename: Mark
authors.initials: M
authors.lastname: Last
authors.affiliation: Shraga Segal Department of Microbiology and Immunology, Faculty of Health Sciences, Ben-Gurion University of the Negev, Beer-Sheva, 84105, Israel.
authors.forename: Eitan
authors.initials: E
authors.lastname: Rubin
mesh_terms: D000465:Algorithms; D019985:Benchmarking; D001943:Breast Neoplasms / Q000517:prevention & control*; D005260:Female; D006801:Humans; D016015:Logistic Models; D000069550:Machine Learning; D009364:Neoplasm Recurrence, Local / Q000517:prevention & control*; D016571:Neural Networks, Computer; D057285:Precision Medicine / Q000379:methods*; D012984:Software
keywords: Breast cancer; Data mining; Reproducible research