blkbox – Integration Of Multiple Machine Learning Approaches To Identify Disease Biomarkers

Machine learning (ML) is a powerful tool to create supervised models that can distinguish between classes and facilitate biomarker selection in high-dimensional datasets, including RNA Sequencing (RNA-Seq). However, it is variable as to which is the best performing ML algorithm(s) for a specific dataset, and identifying the optimal match is time consuming. Researchers from the Garvan Institute of Medical Research have developed blkbox, a software package including a shiny frontend, that integrates nine ML algorithms to select the best performing classifier for a specific dataset. blkbox accepts a simple abundance matrix as input, includes extensive visualization, and also provides an easy to use feature selection step to enable convenient and rapid potential biomarker selection, all without requiring parameter optimization.


A): Schematic workflow and pipeline of blkbox. B) Schematics of the three model types implemented in blkbox.

Feature selection makes blkbox computationally inexpensive while multi-functionality, including nested cross-fold validation (NCV), ensures robust results. blkbox identified algorithms that outperformed prior published ML results. Applying NCV identifies features, which are utilized to gain high accuracy.

Availability – The software is available as a CRAN R package and as a developer version with extended functionality on github (

Guennewig B, Davies Z, Pinese M, Cooper AA. (2017) blkbox: Integration Of Multiple Machine Learning Approaches To Identify Disease Biomarkers. bioRXiv [Epub ahead of print]. [abstract]

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