DTMBio 2017

DTMBIO 17 organizers are pleased to announce that the eleventh DTMBIO will be held in conjunction with CIKM, one of the largest data and text mining conferences. While CIKM presents the state-of-the-art research in informatics with the primary focus on data and text mining, the main focus of DTMBIO is on biomedical and health informatics. DTMBIO delegates will bring forth interesting applications of up-to-date informatics in the context of biomedical research. This year, we are particularly interested in techniques and applications of Big Data Analytics to biomedical and clinical research problems.

Biological researchers face the current challenge of making effective use of the enormous amount of electronic biomedical data in order to better understand and explain complex biological systems. The biomedical data repositories include data in a wide variety of forms, including bibliographic information from electronic medical journals, gene expression data from microarray experiments, protein identification and quantification data from proteomics experiments, genomic sequences gathered by massively parallel sequencing, and patient healthcare records. The ability to automatically and effectively extract, integrate, understand and make use of information embedded in such heterogeneous – structured and unstructured – data remains a challenging task.


We invite the submission of papers that propose ways to address the variety of aspects involved in meeting this challenge. The relevant topics include the following (but not limited to):

  • Biomedical and clinical text mining applications
  • Big bio- or clinical- data analytics
  • Integration of structured and unstructured resources for biomedical applications
  • Information extraction from biomedical and clinical corpora (published literature, grey literature, EHRs, clinical trials, etc.)
  • Information retrieval from large biomedical data collections
  • Gene sequence annotation
  • Protein/RNA structure prediction
  • Medical Ontologies and Text Mining
  • Entity or Concept recognition in text with ontologies
  • Sequence and structural motifs
  • Modeling of biochemical pathways and biological networks
  • Image Mining in Medical and healthcare informatics
  • Data and text Mining solutions in biomedical informatics, for applications such as drug development, system biology, biomedical working processes
  • Information integration for Data and Text Mining
  • Mining multi-relational data
  • Proposal and assessment of novel Text Mining (TM) evaluation strategies
  • Evaluation methods of biomedical applications, shared tasks
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