Categories |
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BIOINFORMATICS
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MACHINE LEARNING
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TRUSTWORTHINESS
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PRIVACY
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Call for Papers |
The 13th International Workshop on Biological Knowledge Discovery from Big Data (BIOKDD2022)
in conjunction with The 33rd DEXA Conferences and Workshops www.dexa.org ***OBJECTIVES*** The workshop "Biomedical Knowledge Discovery from Big Data (BIOKDD)" deals with the dichotomy of potentials and threats of machine learning for biomedical data analysis. There is an ongoing trend to generate and exploit medical data from a variety of distributed sources to achieve high quality personalized healthcare, thereby yielding new challenges regarding domain shift, data quality, privacy security and trust threats. We want to bring together researchers from machine learning, biomedical data analysis, bioinformatics and information security to discuss recent developments, best practices and use cases from ongoing research projects. We especially encourage undergraduate and graduate students to submit their current research findings. Submission Guidelines In order to encourage participation and discussion, this workshop solicits two types of submissions: Regular paper submissions about original work not exceeding 10 pages. Short paper submissions on recent or ongoing work on relevant topics and ideas not exceeding 5 pages. Formatting guidelines: http://www.dexa.org/formatting_guidelines Online Papers Submission: https://easychair.org/conferences/?conf=biokdd2021 ***LIST OF TOPICS*** Topics of BIOKDD 2022 workshop include, but are not limited to: - Biomedical image analysis (segmentation, classification, registration) - Trustworthy and explainable AI for biomedical data - Relational Machine Learning in the context of personalized medicine/biomedical data analysis, such as molecular interaction networks or cellular networks - Integration of personal medical data from multiple sources towards personalized medicine, including novel metrics for multi-modal data set matching - Integration of multi-modal biomedical data such as OMICs, imaging, time-lapse, time series or imaging data - Tackling Domain Shift challenges e.g., due to different life or genomic conditions, imbalance of different subpopulations, data quality, invitro vs. in vivo vs. in silico - Privacy preserving machine learning - Federated learning - Security and protection of shared personal medical data ***COMMITEES*** Program Committee Chair Lukas Fischer PhD, Software Competence Center Hagenberg GmbH (SCCH), Austria Other committees Program Committee members and Steering Committee please go to the workshop's website http://www.dexa.org/biokdd2022 ***PUBLICATION*** All accepted BIOKDD 2022 papers will be published by Springer in their Communications in Computer and Information Science (CCIS). CCIS volumes are indexed in the Conference Proceedings Citation Index (CPCI), part of Clarivate Analytics’ Web of Science; Scopus; EI Engineering Index; Google Scholar; DBLP; etc. ***VENUE*** BIOKDD 2022 workshops will be held at WU (Vienna University of Economics and Business), Vienna Austria. ***CONTACT*** All questions about submissions should be emailed to dexa@iiwas.org |
Summary |
BIOKDD 2022 : The 13th International Workshop on Biological Knowledge Discovery from Big Data will take place in Vienna. It’s a 3 days event starting on Aug 22, 2022 (Monday) and will be winded up on Aug 24, 2022 (Wednesday). BIOKDD 2022 falls under the following areas: BIOINFORMATICS, MACHINE LEARNING, TRUSTWORTHINESS, PRIVACY, etc. Submissions for this Workshop can be made by Mar 7, 2022. Authors can expect the result of submission by May 10, 2022. Upon acceptance, authors should submit the final version of the manuscript on or before Jun 1, 2022 to the official website of the Workshop. Please check the official event website for possible changes before you make any travelling arrangements. Generally, events are strict with their deadlines. It is advisable to check the official website for all the deadlines. Other Details of the BIOKDD 2022
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Credits and Sources |
[1] BIOKDD 2022 : The 13th International Workshop on Biological Knowledge Discovery from Big Data |