Kang Ning

Traditional Chinese Medicine and Diseases

An Omics Big-data Mining Perspective

  • Verlag: Springer Verlag
  • ISBN: 9789811947704
  • 2019, 140 pages


Lieferzeit: ca. 5 - 10 Tage/days

Art.-Nr.: 981194770 Kategorien: ,


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.



Kang Ning

Kang Ning

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