Integrating Incompatible Assay Data Sets with Deep Preference Learning.

ABSTRACT: A large amount of bioactivity assay data is already accumulated in public databases, but the integration of these data sets for quantitative structure-activity relationship (QSAR) studies is not straightforward due to differences in experimental methods and settings. We present an efficient deep-learning-based approach called Deep Preference Data Integration (DPDI). For integrating outcome variables of different assay types, a surrogate variable is introduced, and a neural network is trained such that the total order induced by the surrogate variable is maximally consistent with given data sets. In a task of predicting efficacy of factor Xa inhibitors, DPDI successfully integrated 2959 molecules distributed in 129 assay data sets. In most of our experiments, data integration improved predi

SUBMITTER: Sun X 

PROVIDER: S-EPMC8762726 | biostudies-literature | 2022 Jan

REPOSITORIES: biostudies-literature