BioSGAN: Protein-Phenotype Co-mention Classification Using Semi-Supervised Generative Adversarial Networks

Valuable and relevant information that relates human proteins with their phenotypes in biomedical literature stays hidden from biomedical scientists due to the rapid rise in biomedical publications. Previous studies that developed computational methods to extract this knowledge mostly rely on rule-based linguistic patterns and supervised machine learning approaches. In this work, we propose the us...