A positive and unlabeled learning framework based on extreme learning machine for drug-drug interactions discovery

Abstract

Drug-drug interactions (DDIs) lead to Adverse Drug Reactions (ADRs) in most cases, which increase medical costs tremendously, and may cause medical negligence or even fatal accidents. Many researchers introduce binary classification methods into DDIs discovery task to avoid expensive and inefficient experimental trials. However, we find that drug-drug pairs without DDI labels cannot be simply viewed as negative samples in practice, since unobserved DDIs may be discovered from unlabeled pairs. Therefore, different from traditional positive-negative classification, in this paper, we treat DDIs discovery as a positive-unlabeled learning (PU learning) problem, in which each drug-drug pair is either a positive sample (labeled as DDI) or an unlabeled one (labeled as non-DDI). We propose a PU learning framework based on Extreme Learning Machine (ELM) named PU-ELM, which consists of two components, namely reliable negative extraction and classifier learning. To improve the quality of reliable negative set, we propose a negative extraction method named OC-ELM-RN (One-Class ELM for Reliable Negative extraction). As to the learning module, we first apply the semi-supervised learning strategy in PU-ELM (denoted as PU-ELM-SS) to achieve extremely fast learning speed, and then we propose an entropy based weighted learning method named PU-ELM-EW (PU-ELM with Entropy Weighted learning) to improve the PU learning performance. Extensive experiments are conducted on a synthetic dataset with varied settings. We also apply PU-ELM to a real-world drug dataset. The results indicate that PU-ELM achieves better DDIs discovery ability compared with state-of-the-art methods.