title: Logic models to predict continuous outputs based on binary inputs with an application to personalized cancer therapy.
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.
authors.affiliation: Institute for Systems Biology, Seattle, US.
authors.forename: Theo A
authors.initials: TA
authors.lastname: Knijnenburg
authors.affiliation: Centrum Wiskunde &Informatica, Amsterdam, The Netherlands.
authors.forename: Gunnar W
authors.initials: GW
authors.lastname: Klau
authors.affiliation: European Molecular Biology Laboratory - European Bioinformatics Institute, UK.
authors.forename: Francesco
authors.initials: F
authors.lastname: Iorio
authors.affiliation: Wellcome Trust Sanger Institute, UK.
authors.forename: Mathew J
authors.initials: MJ
authors.lastname: Garnett
authors.affiliation: Wellcome Trust Sanger Institute, UK.
authors.forename: Ultan
authors.initials: U
authors.lastname: McDermott
authors.affiliation: Institute for Systems Biology, Seattle, US.
authors.forename: Ilya
authors.initials: I
authors.lastname: Shmulevich
authors.affiliation: Netherlands Cancer Institute, Amsterdam, and The Faculty of EEMCS, Delft University of Technology, Delft, The Netherlands.
authors.forename: Lodewyk F A
authors.initials: LF
authors.lastname: Wessels
mesh_terms: D000465:Algorithms; D000970:Antineoplastic Agents / Q000627:therapeutic use*; D045744:Cell Line, Tumor; D057225:Data Mining / Q000379:methods; D006801:Humans; D008128:Logic; D008962:Models, Theoretical; D009369:Neoplasms / Q000188:drug therapy*; D057285:Precision Medicine / Q000379:methods; D012680:Sensitivity and Specificity
chemical_list: D000970:Antineoplastic Agents