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Current Open Calls-for-Papers

International Conference on Biomedical and Biological Ontologies: Direct to Journal Track

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The International Conference on Biomedical and Biological Ontologies (ICBO) is a premier annual conference series that brings together researchers, students and professionals involved in the development and application of ontologies and knowledge graphs, and associated artificial intelligence technologies, in all areas of scientific research throughout the life and social sciences, including biology, medicine, diseases, human health, genome biology, human behaviour, environment, biomes, nutrition, food, plants, agriculture and others.

In an exciting partnership with the Journal of Biomedical Semantics (JBMS), we are introducing a direct to journal publication track for contributions to ICBO from 2021 onwards. Papers submitted to the International Conference on Biomedical Ontologies track at the Journal of Biomedical Semantics will be published immediately after acceptance and presented in the annual conference following acceptance. 

The new ICBO Thematic Series at the JBMS is soliciting submissions of novel (not previously published nor concurrently submitted) research papers in the areas of ontology design, development, evaluation and use, ontology interoperability, knowledge graphs, ontology-driven intelligent systems, ontologies for explainable AI, and the application of ontologies to biological and biomedical problems, across the full range of life sciences. In addition, we would like to invite contributions showcasing methods for ontology-based research, including statistical methods, tool support for ontologies and semantic technologies including for the annotation of data, visualisation, analysis, and related applications, and contributions addressing the challenges associated with working with multiple ontologies at the same time, including ontology alignment and matching. Submissions are welcome from a broad range of approaches to ontology building and use.

Submission Details:

Submissions open on December 15th, 2020.

Papers should be submitted directly to the Journal of Biomedical Semantics, following the prescribed author guidelines, and selecting the "International Conference on Biomedical Ontologies (ICBO)" Thematic Series when prompted during submission. All accepted manuscripts will be presented orally at ICBO and at least one author of the manuscript must register at ICBO to present the work. 

The first 20 accepted manuscripts each year will receive a 20% discount on the article processing charge of JBMS. All interested applicants will need to make a partial waiver request during submission noting that they are submitting to the ICBO Thematic Series.

Guest Editors (as of Dec 2020):

  • Janna Hastings (University College London)
  • Robert Hoehndorf (King Abdullah University of Science and Technology)

If you have any further questions or concerns, please reach out to the guest editors for the collection.

Once published, all papers associated with this collection will be posted here

Intelligence Computing for Challenging Clinical Data: Robust, Practical and Trustworthy

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Accuracy, efficiency and transferability are realistic issues in clinical practice, and research hotspots as well. Especially in developing countries, the number of clinicians is not enough to meet the population growth. One doctor has to diagnose dozens of patients every day, with traditional clinical methods. In recent years, artificial intelligence (AI) techniques, e.g., deep learning, tend to mature and have been applied in many clinical tasks successfully. Well-designed networks are able to detect various lesions in medical images, such as pulmonary nodule, diabetic retinopathy, and skin cancer. The accuracy and efficiency have also achieved or exceeded human experts. Introducing AI-aided methods to traditional clinical process would help reduce the burden of doctors, or improve diagnostic ability of inexperienced clinicians.

AI can be a sharp tool with sufficient data and deep architectures. But in many circumstances, models only give a simple result without any evidences, which is still unconvincing for clinical use. Moreover, challenging clinical data (small data, imbalanced data in rare diseases, unobvious lesions in early stage, multiple data source with different distributions, etc.) that cannot meet the requirements of AI algorithms are common. Existing intelligent solutions may be unstable or fail for those data. This requires novel computing techniques with robust, practical and trustworthy.

This Special Issue aims to invite original research papers that tackle challenging clinical data and scenarios, including theoretical research, practical models improvement, clinical-oriented data analysis, deployed digital system, and farsighted intelligent architecture. Submitted papers should clarify the substantively different from work that has already been published, or accepted for publication, or submitted in parallel to other conferences or journals.

The topics of interest include, but are not limited to:

  • Robust models for small or imbalanced clinical data
  • Interpretable algorithms with meaningful clinical outcomes
  • Novel learning paradigms for rare diseases
  • Multi-center/cross-regional clinical data fusion and decision support
  • Collaborative computing on heterogeneous clinical data
  • VR/AR computer-aided diagnosis system
  • Real-time video assessment system
  • Classification, detection, segmentation of lesions and tumors in early stage
  • New measurements and computing methods for early diagnosis
  • New technology for mining sensitive clinical biomarkers or patterns

We also highly recommend the submission of multimedia with each article as it significantly increases the visibility, downloads, and citations of articles.

Important dates:

  • Submission deadline: June 20, 2021
  • Notification of acceptance: Aug 25, 2021
  • Submission of final revised paper: Sep 25, 2021
  • Publication: Upon Acceptance

Guest Editors:

  • Prof. Honghao Gao, Shanghai University, China
  • Prof. Wenbing Zhao, Cleveland State University, Ohio, USA
  • Prof. Yuyu Yin, Hangzhou Dianzi University, China

Once published, all papers associated with this collection will be posted here