Ten papers investigate the use of different computational methods, such as deep learning, big data analysis, advance computation, and network analysis, to address complex data analysis problems in precision medicine. The Asian contributors apply blockchain to a clinical platform, analyze circulating tumor DNA in cancer patients, screen medicinal plant databases for potential drugs, assess high performance computing in tandem mass spectrometry data processing, and propose a hybrid approach for cluster analysis of RNA-seq data. Annotation ©2020 Ringgold, Inc., Portland, OR (protoview.com)
With the increasing availability of omics data and mounting evidence of the usefulness of computational approaches to tackle multi-level data problems in bioinformatics and biomedical research in this post-genomics era, computational biology has been playing an increasingly important role in paving the way as basis for patient-centric healthcare. Two such areas are: (i) implementing AI algorithms supported by biomedical data would deliver significant benefits/improvements towards the goals of precision medicine (ii) blockchain technology will enable medical doctors to securely and privately build personal healthcare records, and identify the right therapeutic treatments and predict the progression of the diseases. A follow-up in the publication of our book Computation Methods with Applications in Bioinformatics Analysis (2017), topics in this volume include: clinical bioinformatics, omics-based data analysis, Artificial Intelligence (AI), blockchain, big data analytics, drug discovery, RNA-seq analysis, tensor decomposition and Boolean network.
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