The emerging field of translational biomedical informatics aims to develop innovative analytic methods and tools to transform large amount of biomedical and genomic data into meaningful clinical and biological knowledge. It will largely accelerate biomedical knowledge discovery and improve health services and patient care outcomes. To achieve the holly grail of translational biomedical informatics research, clinical observations and biological experiments should be analyzed holistically. Unfortunately, the huge volume of data of heterogeneous nature, and different cultures in biomedical research and clinical practice impose a severe obstacle – even bioinformatics and medical informatics (also called clinical informatics sometimes) remain as separate fields for long time. Methods, technologies and applications that build on biomedical and clinical data integration are in great need.
The broader context of the workshop includes artificial intelligence, information retrieval, machine learning, natural language processing, and integrative analysis of biological and clinical data. The purpose of this workshop is to provide a forum for researchers to share their research methodologies and tools on managing, analyzing, and discovering knowledge from diverse and complex biomedical and clinical data. Submissions are invited to address the needs for developing innovative methods and meaningful applications that can potentially lead to significant advances in translational biomedical informatics research.
The topics of interest include but not limited to:
Application of data mining approaches in precision medicine
Machine learning and statistical approaches for biomedical and clinical data mining
Natural language processing in clinical data
Information retrieval in clinical data
Topic detection and information extraction in biomedical and clinical data
Integration of heterogeneous biomedical data sources
Semantic annotation on biomedical data
Semantic reasoning and inference on biomedical data
Biomedical data representation utilizing Ontology matching and data model schema
Deep Learning on biomedical and clinical data
Large-scale biomedical data management system
Phenotypic-Genotypic association detection
Network and Systems Biology
Biomedical Network motif analysis
Computational genetics, genomics and proteomics
Computational drug discovery
Computer-aided detection and diagnosis
Pharmacogenomics
Multi-omics data integration
11月13日
2017
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