Complex biological systems are traditionally modelled as graphs of interconnected
biological entities. These graphs, ie biological knowledge graphs, are then processed using …

Deep learning models contributed to reaching unprecedented results in prediction and
classification tasks of Artificial Intelligence (AI) systems. However, alongside this notable …

Background and purpose Contouring of organs at risk (OARs) is an important but time
consuming part of radiotherapy treatment planning. The aim of this study was to investigate …

Motivation Computational approaches for predicting drug–target interactions (DTIs) can
provide valuable insights into the drug mechanism of action. DTI predictions can help to …

Neural link predictors are immensely useful for identifying missing edges in large scale
Knowledge Graphs. However, it is still not clear how to use these models for answering …

The correlation of phenotypic outcomes with genetic variation and environmental factors is a
core pursuit in biology and biomedicine. Numerous challenges impede our progress: patient …

Most of the existing medicine recommendation systems that are mainly based on electronic
medical records (EMRs) are significantly assisting doctors to make better clinical decisions …

Reproducible computational research (RCR) is the keystone of the scientific method for in
silico analyses, packaging the transformation of raw data to published results. In addition to …

Background Biomedical translational science is increasingly using computational reasoning
on repositories of structured knowledge (such as UMLS, SemMedDB, ChEMBL, Reactome …

Recently, novel machine-learning algorithms have shown potential for predicting
undiscovered links in biomedical knowledge networks. However, dedicated benchmarks for …