Machine Learning for Biomedical Application

Dear Colleagues,

Biomedicine is a multidisciplinary branch of medical science that consists of many scientific disciplines, e.g., biology, biotechnology, bioinformatics, and genetics; moreover, it covers various medical specialties. In recent years, a huge development of this field of science has been observed. The consequence of this is not only achievements that allow better understanding of the principles of the human body functioning at various levels (cellular, anatomical, and physiological), but also a large amount of data generated, among others as a result of analyses of the human genome or the processing, analysis, and recognition of a wide class of biomedical signals and images obtained through increasingly advanced medical imaging devices. The analysis of these data requires the use of advanced IT methods, which include those related to the use of artificial intelligence, and in particular machine learning.

The Topical Collection will include applications of machine learning in processing, analysis, and recognition of biomedical data. Specific attention will be given to recently developed deep learning techniques and their application in extracting essential information from large biomedical databases. Hence, proposed topics include but are not limited to the following applications of machine learning:

• Genomic sequence determinations and analysis of gene expression patterns;
• Processing and analysis of biomedical signals and images;
• Modifying living organisms according to human purposes;
• Improving cell and tissue culture technologies;
• Development of deep learning architectures in analysis of biomedical data.

Prof. Dr. Michał Strzelecki
Dr. Pawel Badura
Collection Editors

Manuscript Submission Information

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