The primary goal of drug safety researchers and regulators is to promptly identify adverse drug reactions.Doing so may in turn prevent or reduce the harm to patients and ultimately improve public health.Evaluating and monitoring drug safety (i.e., pharmacovigilance) involves analyzing an ever growing collection of spontaneous reports from health professionals, physicians, and pharmacists, and information voluntarily submitted by patients.In this scenario, facilitating analysis of such reports via automation has the potential to rapidly identify safety signals.Unfortunately, public resources for developing natural language models for this task are scant.We present PHEE, a novel dataset for pharmacovigilance comprising over 5000 annotated events from medical case reports and biomedical literature, making it the largest such public dataset to date.We describe the hierarchical event schema designed to provide coarse and fine-grained information about patients' demographics, treatments and (side) effects.Along with the discussion of the dataset, we present a thorough experimental evaluation of current state-of-the-art approaches for biomedical event extraction, point out their limitations, and highlight open challenges to foster future research in this area 1 .

Drug-target interaction (DTI) prediction is important in drug discovery and chemogenomics studies. Machine learning, particularly deep learning, has advanced this area significantly over the past few years. However, a significant gap between the performance reported in academic papers and that in practical drug discovery settings, e.g. the random-split-based evaluation strategy tends to be too optimistic in estimating the prediction performance in real-world settings. Such performance gap is largely due to hidden data bias in experimental datasets and inappropriate data split. In this paper, we construct a low-bias DTI dataset and study more challenging data split strategies to improve performance evaluation for real-world settings. Specifically, we study the data bias in a popular DTI dataset, BindingDB, and re-evaluate the prediction performance of three state-of-the-art deep learning models using five different data split strategies: random split, cold drug split, scaffold split, and two hierarchical-clustering-based splits. In addition, we comprehensively examine six performance metrics. Our experimental results confirm the overoptimism of the popular random split and show that hierarchical-clustering-based splits are far more challenging and can provide potentially more useful assessment of model generalizability in real-world DTI prediction settings.

miRNAs (microRNAs) are a class of endogenous small RNAs that are thought to negatively regulate protein production. Aberrant expression of many miRNAs is linked to cancer and other diseases. Little is known about the factors that regulate the expression of miRNAs. We have identified numerous regulatory elements upstream of miRNA genes that are likely to be essential to the transcriptional and posttranscriptional regulation of miRNAs. Newly identified regulatory motifs occur frequently and in multiple copies upstream of miRNAs. The motifs are highly enriched in G and C nucleotides, in comparison with the nucleotide composition of miRNA upstream sequences. Although the motifs were predicted using sequences that are upstream of miRNAs, we find that 99% of the top-predicted motifs preferentially occur within the first 500 nucleotides upstream of the transcription start sites of protein-coding genes; the observed preference in location underscores the validity and importance of the motifs identified in this study. Our study also raises the possibility that a considerable number of well-characterized, disease-associated transcription factors (TFs) of protein-coding genes contribute to the abnormal miRNA expression in diseases such as cancer. Further analysis of predicted miRNA-protein interactions lead us to hypothesize that TFs that include c-Myb, NF-Y, Sp-1, MTF-1, and AP-2alpha are master-regulators of miRNA expression. Our predictions are a solid starting point for the systematic elucidation of the causative basis for aberrant expression patterns of disease-related (e.g., cancer) miRNAs. Thus, we point out that focused studies of the TFs that regulate miRNAs will be paramount in developing cures for miRNA-related diseases. The identification of the miRNA regulatory motifs was facilitated by a new computational method, K-Factor. K-Factor predicts regulatory motifs in a set of functionally related sequences, without relying on evolutionary conservation.