RT Journal Article
SR Electronic
T1 Logic models to predict continuous outputs based on binary inputs with an application to personalized cancer therapy
JF bioRxiv
FD Cold Spring Harbor Laboratory Press
DO 10.1101/036970
A1 Knijnenburg, Theo
A1 Klau, Gunnar
A1 Iorio, Francesco
A1 Garnett, Mathew
A1 McDermott, Ultan
A1 Shmulevich, Ilya
A1 Wessels, Lodewyk
YR 2016
UL http://biorxiv.org/content/early/2016/01/15/036970.abstract
AB 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.