Translational medicine, aimed at bridging the gap between biomedical scientific knowledge and clinical practice has changed the way we use rapidly growing information from biomedical research and bring it closer to clinical practice. Software technologies play essential role in understanding and manipulating the meaning of biomedical data and translate it into semantic suitable for clinical practice. This special session focuses semantic technologies, plus predictive analytics/learning technologies which play an important role in collecting and understanding the semantic of biomedical knowledge and address problems in translational medicine.
TOPICS OF INTEREST:
1) The applications of semantic and predictive technologies for drug discoveries, repurposing, drug-to-drug interactions and drug recommendation discoveries.
2) Exploiting semantic predications, semantic matching and biomedical ontologies for inferring knowledge in translational medicine.
3) Semantic matching of phenotypes and therapeutic targets, clinical trials and patient eligibility criteria; relationship between genotypic, phenotypic and environmental knowledge.
4) Learning and predictive technologies for creating meaningful insight into biomedical research data; Semantic classifiers and feature selections for ML in translational biomedicine.
5) Deep learning meets semantic technologies and reasoning: applications in translational medicine, cheminformatics and pharmacology.