Biological Ontologies and Knowledge bases


IEEE BIBM 2018 Workshop · Madrid · Spain · Dec 3-6, 2018.

In omics-era of the life sciences, it is cost-effective to collect diverse types of genome wide data, which represent the information at various levels of biological systems, including data about genome, transcriptome, epigenome, proteome, metabolome, molecular imaging, molecular pathways, different population of people and clinical/medical records. Currently, big challenge is to represent and use the knowledge contained in the massive data.

A bio-ontology provides standardized and structured vocabulary terms for the scientific community to describe biomedical entities in a domain. In recent years, numerous biomedical ontologies have been developed to represent knowledge about anatomy, molecular function, human phenotype, disease, clinical diagnosis and other areas.  Biomedical Ontologies have been proven very useful for knowledge representation, entity annotation, data sharing and data integration et al. in biomedical research.

Knowledge bases are increasingly being used to extract deep biological knowledge and understanding from massive biological data. Knowledge bases can provide information on underlying mechanisms, which statistical inference methods cannot gain insight into. This improvement is largely due to knowledge bases providing a validated biological context for interpreting the ocean of omics.

The biomedical ontologies and knowledge bases workshop provides a vibrant environment for researchers to share their research finding, report novel methods, and discuss the challenges and opportunities in the related fields.

Research Topic

Papers are welcome in multiple areas, such as the applications of biological ontologies and knowledge bases, newly developed Bio-Ontologies and Knowledge bases (databases), and the method or standards of ontology and knowledge base development. Papers are solicited on, but not limited to, the following topics:

  • Semantic web enabled applications
  • Systematic inference of ontologies

  • Bio-curation platforms
  • Application of ontologies in biology
  •  Application of knowledge bases in biology
  • Role of bio-ontologies in the learning health system
  • Deep learning with ontologies or knowledge bases
  • Automated Annotation method
  • Use of ontologies in text mining applications
  • Application of text mining in knowledge bases
  • Ontology mapping and extension
  • Research in Ontology Evaluation
  • Newly developed ontology and knowledge bases

Submission

  • We call for original and unpublished research contributions to the workshop
  • Please submit a full length paper (up to 6 page IEEE 2-column format) through the online submission system (you can download the format instruction here (http://www.ieee.org/conferences_events/conferences/publishing/templates.html). Electronic submissions in PDF format are required. 
  • Online submission system is at  https://wi-lab.com/cyberchair/2018/bibm18/scripts/ws_submit.php?subarea=S
  • All accepted papers will be published in IEEE Xplore Digital Library (EI index). Extension version of accepted papers will be published in bmc bioinformatics (IF: 2.43) following the journal's publication policy. All accepted papers will have to be presented by one of the authors at the workshop.

Program

Coming Soon.

The special issue of BiOK2017 on BMC Bioinformatics: https://bmcbioinformatics.biomedcentral.com/articles/supplements/volume-19-supplement-5

Organizers

This workshop will be organized by Dr. Jiajie Peng and Dr. Jin Chen. Dr. Jiajie Peng is Associate Professor of School of Computer Science, Northwestern Polytechnical University. His research focuses on data mining, multi-omics data analysis, data-driven ontology construction, gene ontology based semantic similarity, biological network analysis and construction. Dr. Jin Chen is Associate Professor of the Institute of Biomedical Informatics, College of Medicine, University of Kentucky. His research focuses on the development of data mining, artificial intelligence and computer vision algorithms to solve basic biological problems. He has developed serials of algorithms for phenotype modeling, including inter-functional clustering, heterogeneity pattern recognition, phenotype ontology construction, phenotype data quality control, gene ranking, and visual data mining. He is also interested in biological network reconstruction using high-dimensional omics data. The tools include transcriptional regulatory module identification, organelle communication pattern mining, and ontology reconstruction.

Committee members

TBD