Emerging Methods and Applications in Bio-Data Science Research
The editors are inviting you to submit papers for the special issue - "Emerging Methods and Applications in Bio-data Science Research." The submission deadline is December 31, 2024. Springer will publish the volume. Please see the areas that will be covered in the volume in the description below -
The biomedical field stands on the precipice of an unprecedented data revolution. Vast and diverse bio-data sets – registry platforms, large-scale surveys, electronic medical records, and multi-omics analyses – paint a vibrant picture of human health and disease. Yet, unlocking the
potential of this information deluge requires sophisticated tools and innovative approaches. This is where Artificial Intelligence (AI) and data mining techniques take center stage, offering a powerful symphony of analytical tools to extract valuable insights, predict health outcomes, and personalize healthcare. This special issue delves into the cutting-edge landscape of AI and data mining applications within bio-data science research. We aim to: • Showcase the potential of these techniques in harnessing the diverse wealth of bio-data – registry trends, survey insights, EMR narratives, and multiomics symphonies. • Highlight the specific AI and data mining algorithms and models driving breakthroughs in disease prediction, personalized medicine, and public health interventions. We invite scholars and researchers across the spectrum of bio-data science to contribute their expertise. We welcome original research articles, review papers, and critical perspectives on: • AI-powered analysis of registry data: Unveiling disease patterns, predicting risk, and personalizing prevention strategies. • Mining insights from large surveys: Utilizing natural language processing, sentiment analysis, and network approaches to understand public health concerns and inform targeted interventions. • Extracting knowledge from EMRs: Predicting disease complications, personalized treatment plans, and leveraging deep learning for accurate medical image diagnosis. • Harnessing multiomics data: Unraveling high throughput genomics, transcriptomics, proteomics data for identifying disease subtypes, and driving personalized medicine advancements. This special issue aspires to: • Spark a vibrant dialogue on the transformative potential of AI and data mining in bio-data science research. • Offer a comprehensive resource for researchers and clinicians to leverage these cutting-edge tools in their studies and practice. • Pave the way for a future where data-driven insights empower personalized healthcare, shape effective public health strategies, and fuel groundbreaking discoveries in disease mechanisms and prevention. Beyond the technicalities of AI and data mining, we encourage submissions that address the broader implications of this field: • The economic and social impact of AI-driven healthcare transformations. • The workforce challenges and educational needs for a data-driven bio-science landscape. • The importance of global collaboration and open data initiatives in maximizing the benefits of bio-data research. By embracing the opportunities and challenges presented by AI and data mining, this special issue aims to orchestrate a symphony of scientific progress, illuminating the path towards a healthier future for all.
https://link.springer.com/collections/jigicdhdjh
Editors: Shesh N Rai, Dwijesh Chandra Mishra, Sudhir Srivastava, and Anand Seth
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Anand Seth
CEO and Head R&D
SK Patent Associates, LLC
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