Bioinformatics.org
[University of Birmingham]
[Patsnap]
Not logged in
  • Log in
  • Bioinformatics.org
    Membership (44429+) Group hosting [?] Wiki
    Franklin Award
    Sponsorships

    Careers
    About bioinformatics
    Bioinformatics jobs

    Research
    All information groups
    Online databases Online analysis tools Online education tools More tools

    Development
    All software groups
    FTP repository
    SVN & CVS repositories [?]
    Mailing lists

    Forums
    News & Commentary
  • Submit
  • Archives
  • Subscribe

  • Jobs Forum
    (Career Center)
  • Submit
  • Archives
  • Subscribe
  • News & Commentary - Message forums

    Events: CfP: IEEE BIBM Workshop on Expository Representation Learning of Biomedical Data (ERLBD)
    Submitted by Haluk Dogan; posted on Saturday, September 07, 2019

    Nov. 18-21, 2019
    San Diego, CA, USA
    BIBM-ERLBD: http://sbbi-panda.unl.edu/bibm2019/
    IEEE BIBM Workshop on Expository Representation Learning of Biomedical Data (ERLBD) to be held in conjunction with the 2019 IEEE International Conference on Bioinformatics and Biomedicine (IEEE BIBM 2019): http://ieeebibm.org/BIBM2019/

    In today's world data is produced at a mind-boggling volume and pace. According to marketing reports 90 percent of the data in the world was produced within the past two years. With the recent advances in high-throughput technologies, OMICS data in the biomedical field would be one of the biggest contributor to this everlasting growth. These large volumes of data are widely accessible; however, they are mostly unstructured while having the potential to deepen our understanding of complex problems from disease outbreak to disease diagnosis and treatment. Statistical and machine learning methods can help expose the hidden value of unstructured data, improving prediction accuracy, and build predictive models that go beyond human performance. Recently, there is a surge of new techniques in the context of discovering latent structure in high dimensional data such as causal relations in multifaceted regulation networks, genotype and phenotype association, genomics signatures, and risk factors etc. However, it can still be a long way to obtain satisfactory results in scalable learning with increasing data size and complexity. Furthermore, a wide variety of technologies produce heterogeneous data shedding light on different aspects of complex biological systems. Emergence of a more complete picture of biological systems depends on successful methods for integration of data from these different perspectives. With this workshop, we aim to encourage researchers to develop new methodologies, analytical models, and high-throughput computing workflow that best utilize various types of biomedical data in ways that meaningful structures present but hidden in data can be revealed.

    RESEARCH TOPICS

    The potential topics include, but not limited to, the following:
    • Data-centric models for diagnosis and classification of complex human disease such as cancers and obesity
    • Learning based prediction models for drug response assessment
    • Integrative models for identification of dynamic and multi-level biological interaction networks (proteins, non-coding genes, metabolites)
    • Integrative models for discovery of disease associated cell communication
    • Complex phylogenetic models to elucidate cancer genome evolution
    • High-throughput computing workflow development for data mining, visualization, and interpretation
    • High-throughput computing workflow development for biomedical image processing classification
    • Integrative models for visualization and analysis of data in neuroscience
    • Information fusion based modeling for reverse engineering biological networks
    • Inferring genomic signatures from sequencing and expression data
    • Modeling and simulation of complex biological processes in high-throughput computing environments

    CALL FOR PAPERS

    We invite you to submit papers with unpublished, original research describing recent advances on the areas related to this workshop. All papers will undergo peer review by the conference program committee. All papers accepted will be included in the Workshop Proceedings published by the IEEE Computer Society Press and will be available at the workshops. Authors of selected papers will be invited to extend their papers for submission to special issues in prestigious Journals.

    Paper Submission:
    Please submit a full-length paper (up to 8 pages in IEEE two-column format) through the online submission system. Electronic submissions in pdf format are required.

    For paper submission click on the following link:
    https://wi-lab.com/cyberchair/2019/bibm19/scripts/submit.php?subarea=S22&undisplay_detail=1&wh=/cyberchair/2019/bibm19/scripts/ws_submit.php

    TRAVEL SUPPORT

    Funds are available for limited travel fellowships to support students and researchers from underrepresented minority groups.

    IMPORTANT DATES

    Due date for full workshop paper submission: October 01, 2019 11:59:59 PM EST
    Notification of paper acceptance to authors: Oct 15, 2019
    Camera-ready of accepted papers: Nov 1, 2019
    Workshops: Nov 18-21, 2019

    Workshop Chair: Juan Cui

    Expanded view | Monitor forum | Save place

    Start a new thread:
    You have to be logged in to post a reply.

     

    Copyright © 2024 Scilico, LLC · Privacy Policy