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

Theo A. Knijnenburg, Gunnar W Klau, Francesco Lorio, Mathew J. Garnett, Ultan McDermott, I Shmulevich, Lodewyk Wessels

Research output: Contribution to journalArticleScientificpeer-review

20 Citations (Scopus)
19 Downloads (Pure)

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. 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 continuous output variable. 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 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.
Original languageEnglish
Article number36812
Pages (from-to)1-14
Number of pages14
JournalScientific Reports
DOIs
Publication statusPublished - 2016

Keywords

  • Cancer genomics
  • Logic rates
  • Machine learning

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