Tuesday, April 2, 2024

[DMANET] CFP-IEEE Journal of Biomedical and Health Informatics (IF:7.7)-SI: Current Trends and Future Directions in Biomedical Data Science

IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS (J-BHI )

Special Issue on
"Current Trends and Future Directions in Biomedical Data Science"
*Deadline for Submission: June 17, 2024*

This special issue is dedicated to Professor Panos M. Pardalos on his 70th
birthday to celebrate his contributions to this field and provide a
platform for researchers to present their latest research findings, ideas,
and future directions in biomedical data science.
The topics of the special issue include, but are not limited to, the
following expected applications:
• Explainable AI (XAI): In healthcare, it is critical to have
interpretable AI models that provide explanations for their predictions.
XAI is particularly important for gaining the trust of healthcare
professionals and ensuring patient safety.
• Multi-Omics Integration: Integrating data from various omics fields,
such as genomics, proteomics, and metabolomics, provides a comprehensive
view of biological systems. Biomedical data scientists are developing
methods to combine and analyze these diverse datasets for a more holistic
understanding of health and disease.
• Microscopic and Biomedical Imaging: The application of data science
techniques to image analysis, segmentation, registration, and fusion in
microscopy and biomedical imaging.
• Next-Generation Sequencing: Using data science techniques to process
and analyze genomic data from nextgeneration sequencing technologies.
• Artificial Intelligence for Biomedical Data: The application of
artificial intelligence techniques, including machine learning and deep
learning, to analyze and interpret biomedical data
. • Biomedical Big Data Analytics: The development of advanced analytics
techniques and tools to process and analyze large-scale biomedical data.
• Optimization and Operation Research for Biomedical Data: Developing
optimization models and algorithms to improve healthcare operations and
decision-making processes.
• Deep Learning for Biomedical Data: Applying deep learning techniques to
biomedical data, including medical imaging and genomic data.
• Additionally, we encourage submissions of surveys and future-oriented
papers in biomedical data science
=====
Guest Editors Hossein Moosaei, Roohallah Alizadehsani, Mario Rosario
Guarracino, Jerry Chun-Wei Lin

For more details, please visit:

https://www.embs.org/jbhi/wp-content/uploads/sites/18/2023/10/JBHI_Biomedical-Data-Science_SI_Moosaei.edited.pdf


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