Traditional Chinese Medicine and Diseases - An Omics Big-data Mining Perspective

This book focuses on the multi-omics big-data integration, the data-mining techniques and the cutting-edge omics researches in principles and applications for a deep understanding of Traditional Chinese Medicine (TCM) and diseases from the following aspects: (1) Basics about multi-omics data and analytical methods for TCM and diseases. (2) The needs of omics studies in TCM researches, and the basic background of omics research in TCM and disease. (3) Better understanding of the multi-omics big-data integration techniques. (4) Better understanding of the multi-omics big-data mining techniques, as well as with different applications, for most insights from these omics data for TCM and disease researches. (5) TCM preparation quality control for checking both prescribed and unexpected ingredients including biological and chemical ingredients. (6) TCM preparation source tracking.  (7) TCM preparation network pharmacology analysis. (8) TCM analysis data resources, web services, and visualizations. (9) TCM geoherbalism examination and authentic TCM identification. 

Traditional Chinese Medicine has been in existence for several thousands of years, and only in recent tens of years have we realized that the researches on TCM could be profoundly boosted by the omics technologies. Devised as a book on TCM and disease researches in the omics age, this book has put the focus on data integration and data mining methods for multi-omics researches, which will be explained in detail and with supportive examples the 'What', 'Why' and 'How' of omics on TCM related researches. It is an attempt to bridge the gap between TCM related multi-omics big data, and the data-mining techniques, for best practice of contemporary bioinformatics and in-depth insights on the TCM related questions.

Dr. Kang Ning, Professor and Director of Department of Bioinformatics and Systems Biology, School of Life Science and Technology, Huazhong University of Science and Technology. Dr. Ning obtained his BS in Computer Science from University of Science and Technology of China, and PhD in Bioinformatics from National University of Singapore. He obtained his Post-Doc training in Bioinformatics at University of Michigan, Ann Arbor. He has been devoting to bioinformatics research for more than 20 years focusing on omics big-data integration, microbiome analyses, and single-cell analyses. His current research interests include AI methods for multi-omics especially metagenomics data mining and their applications. He is also interested in synthetic biology and TCM omics. Dr. Ning as the leading or corresponding author, published over 100 research articles and reviews on leading journals including PNAS, Gut, Genome Biology, Genome Medicine, Nucleic Acids Research and Bioinformatics, with more than 4,000 citations in total. He is the committee member of several national bioinformatics and biology big-data committees in China. He serves as an editorial board member of the journals including Genomics Proteomics and Bioinformatics, Microbiology Spectrum and Scientific Reports, and served as reviewers for several international funding agencies including UK-BBSRC and UK-NERC.