Jan 15, 2016

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

BioRxiv : the Preprint Server for Biology
Theo A KnijnenburgLodewyk F A Wessels

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 rep...Continue Reading

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Mentioned in this Paper

Drug Response
Size
Genes
Cancer Treatment
Tumor Cells, Malignant
Cell Line, Tumor
Logic Model

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