Nephrology practice and research frequently require computational analysis of patient facts
documented only in past clinical notes, ranging from the identification of basic facts such as …

The active gene annotation corpus (AGAC) was developed to support knowledge discovery
for drug repurposing. Based on the corpus, the AGAC track of the BioNLP Open Shared …

Text mining (TM) is a semi-automatized, multi-step process, able to turn unstructured into
structured data. TM relevance has increased upon machine learning (ML) and deep …

Epilepsy is a common neurological disorder that affects mammalian species including
human beings and dogs. In order to discover novel drugs for the treatment of canine …

We introduce a biomedical information extraction (IE) pipeline that extracts biological
relationships from text and demonstrate that its components, such as named entity …

Background Semantic web technology has been applied widely in the biomedical
informatics field. Large numbers of biomedical datasets are available online in the resource …

Emphasis in the tensor literature on random embeddings (tools for low-distortion dimension
reduction) for the canonical polyadic (CP) tensor decomposition has left analogous results …

Background Drug repurposing is to find new indications of approved drugs, which is
essential for investigating new uses for approved or investigational drug efficiency. The …

Tensors are higher-order, multiway generalizations of matrices. Many datasets in machine
learning problems can be naturally organized in tensor form, and leveraging this multilinear …

Understanding the pathogenesis of genetic diseases through different gene activities and
their relations to relevant diseases is important for new drug discovery and drug …