An Active Gene Annotation Corpus and Its Application on Antiepilepsy Drug Discovery After mutation, a gene either gains or loses a function, which is considered to be a critical piece of information for understanding a related pathology and thus for drug discovery. By classifying association of genes and phenotypes using the information of gainoffunction or lossoffunction, the exploration for drug repurposing can be guided in a much more efficient way. We present the active gene annotation corpus (AGAC), which contains 500 manually annotated abstracts collected from PubMed. Five bioconcept labels and three regulatory concept labels were designed for concept level annotation. Furthermore, two relation types, xe2x80x9cThemexe2x80x9d and xe2x80x9cCausexe2x80x9d, were designed to interlink regulatory concepts with their thematic and causal elements. We evaluated AGAC from three aspects, the results of which indicate the high quality of the annotation. Eventually, a PubMedwide case study was performed to show that by using AGAC the process of antiepilepsy drug discovery can be enhanced. After text retrieval, filtering, text classification and matching with DrugBank entries, 281 genedrug pairs and 112 drugs were obtained and 30 out of 112 were recorded in databases. Among 10 newly predicted multitarget drugs which were not recorded in databases, 6 of them were found to be related to epilepsy with literature support, i.e., Oxazepam, Temazepam, Halazepam, Prazepam, Zolpidem and Thiamylal. The result of the case study support the potential of AGAC for enhancing knowledge discovery for drug repurposing.

A Systematic Framework for Drug Repurposing based on Literature Mining Drug repurposing, referring to the discovery of new indications for an existing drug, has become an important aspect of drug development. This study seeks to extract hidden diseasedrug associations from existing literature. Specifically, we propose a systematic framework assuming that a drug has a new use for a certain disease if the clinical disease symptoms and clinical drug effects complement each other. Via literature mining, we generate two vectors to describe clinical disease symptoms and clinical drug effects. Then we use a logistic regression model to predict how effectively the drug counteracts the disease based on the generated diseasedrug vectors. Comprehensive experiments show the effectiveness of proposed framework. According to our results, there are many hidden diseasedrug associations which can be the candidates for drug repurposing.