- Big Data for Omics and Bioimages Integration:
This theme explores the integration of big data from omics (genomics, proteomics, etc.) and bioimages (medical images, microscopy images, etc.). It involves developing methods and algorithms to combine and analyze these diverse data sources for comprehensive biomedical analysis. - Deep Learning for Biodata Analysis:
This theme focuses on utilizing deep learning techniques for the analysis of biodata. It involves developing deep neural network architectures and algorithms to extract meaningful insights from large-scale biomedical datasets, enabling more accurate diagnostics and treatment strategies. - Bio Data Visualization:
This theme concentrates on developing visualization techniques for bio data. It involves designing interactive and informative visualizations to aid researchers and clinicians in exploring and understanding complex biomedical data, facilitating data-driven decision-making. - Extended Reality for Biomedical Applications:
This theme research topic investigates the design and the implementation of extended reality applications in the field of biomedical research and healthcare. It involves developing augmented extended reality systems and human machine interfaces that can enhance medical imaging, surgical planning, and other biomedical applications. - Software repository integration and mining.
This theme explores the integration software repositories along with the mining of the integrated information to support different software engineering activities such as the maintenance of software systems, to analyze and understand software development processes, and software runtime behavior; to support the code analysis, reverse engineering and software comprehension; to empirically validate the proposal of novel software engineering methodologies and techniques; to support fault prediction.
