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blkbox: Integration of multiple machine learning approaches to identify disease biomarkers

Boris Guennewig, Zachary Davies, Mark Pinese, Antony A Cooper
doi: https://doi.org/10.1101/123430
Boris Guennewig
1Garvan Institute of Medical Research, Darlinghurst, New South Wales, Australia
2St Vincent’s Clinical School BABS UNSW Australia, Sydney, New South Wales, Australia
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Zachary Davies
1Garvan Institute of Medical Research, Darlinghurst, New South Wales, Australia
3BABS UNSW Australia, Sydney, New South Wales, Australia
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Mark Pinese
1Garvan Institute of Medical Research, Darlinghurst, New South Wales, Australia
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Antony A Cooper
1Garvan Institute of Medical Research, Darlinghurst, New South Wales, Australia
2St Vincent’s Clinical School BABS UNSW Australia, Sydney, New South Wales, Australia
3BABS UNSW Australia, Sydney, New South Wales, Australia
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Abstract

Motivation 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. blkbox is 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.

Results 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 (https://github.com/gboris/blkbox).

Contact b.guennewig{at}garvan.org.au

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC 4.0 International license.
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Posted April 03, 2017.
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blkbox: Integration of multiple machine learning approaches to identify disease biomarkers
Boris Guennewig, Zachary Davies, Mark Pinese, Antony A Cooper
bioRxiv 123430; doi: https://doi.org/10.1101/123430
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blkbox: Integration of multiple machine learning approaches to identify disease biomarkers
Boris Guennewig, Zachary Davies, Mark Pinese, Antony A Cooper
bioRxiv 123430; doi: https://doi.org/10.1101/123430

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