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Logic models to predict continuous outputs based on binary inputs with an application to personalized cancer therapy

Theo Knijnenburg, Gunnar Klau, Francesco Iorio, Mathew Garnett, Ultan McDermott, Ilya Shmulevich, Lodewyk Wessels
doi: https://doi.org/10.1101/036970
Theo Knijnenburg
Institute for Systems Biology, Seattle, US
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Gunnar Klau
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Francesco Iorio
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Mathew Garnett
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Ultan McDermott
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Ilya Shmulevich
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Lodewyk Wessels
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Abstract

Mining large datasets using machine learning approaches often leads to models that are hard to interpret and not amenable to the generation of hypotheses that can be experimentally tested. Finding ‘actionable knowledge’ is becoming more important, but also more challenging as datasets grow in size and complexity. We present ‘Logic Optimization for Binary Input to Continuous Output’ (LOBICO), a computational approach that infers small and easily interpretable logic models of binary input features that explain a binarized continuous output variable. Although the continuous output variable is binarized prior to optimization, the continuous information is retained to find the optimal logic model. Applying LOBICO to a large cancer cell line panel, we find that logic combinations of multiple mutations are more predictive of drug response than single gene predictors. Importantly, we show that the use of the continuous information leads to robust and more accurate logic models. LOBICO is formulated as an integer programming problem, which enables rapid computation on large datasets. Moreover, LOBICO implements the ability to uncover logic models around predefined operating points in terms of sensitivity and specificity. As such, it represents an important step towards practical application of interpretable logic models.

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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-ND 4.0 International license.
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Posted January 15, 2016.
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Logic models to predict continuous outputs based on binary inputs with an application to personalized cancer therapy
Theo Knijnenburg, Gunnar Klau, Francesco Iorio, Mathew Garnett, Ultan McDermott, Ilya Shmulevich, Lodewyk Wessels
bioRxiv 036970; doi: https://doi.org/10.1101/036970
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Logic models to predict continuous outputs based on binary inputs with an application to personalized cancer therapy
Theo Knijnenburg, Gunnar Klau, Francesco Iorio, Mathew Garnett, Ultan McDermott, Ilya Shmulevich, Lodewyk Wessels
bioRxiv 036970; doi: https://doi.org/10.1101/036970

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