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Traditional Chinese medicine and dis...

Ning, Kang.

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Traditional Chinese medicine and diseases = an omics big-data mining perspective /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Traditional Chinese medicine and diseases/ edited by Kang Ning.
其他題名:	an omics big-data mining perspective /
其他作者:	Ning, Kang.
出版者:	Singapore :Springer Nature Singapore : : 2022.,
面頁冊數:	ix, 139 p. :ill., digital ;24 cm.
內容註:	Chapter 1. Introduction to multi-omics data and analytical methods for TCM and diseases -- Chapter 2. TCM and diseases: the needs for multi-omics -- Chapter 3. TCM related multi-omics data integration techniques -- Chapter 4. TCM related multi-omics data mining techniques -- Chapter 5. TCM preparation quality control: biological and chemical ingredient analysis -- Chapter 6. TCM preparation source tracking -- Chapter 7. TCM preparation network pharmacology analysis -- Chapter 8. TCM analysis data resources, web services and visualizations -- Chapter 9. TCM geoherbalism examination and authentic TCM identification.
Contained By:	Springer Nature eBook
標題:	Medicine, Chinese. -
電子資源:	https://doi.org/10.1007/978-981-19-4771-1
ISBN:	9789811947711

Traditional Chinese medicine and diseases = an omics big-data mining perspective /
Traditional Chinese medicine and diseasesan omics big-data mining perspective /[electronic resource] :edited by Kang Ning. - Singapore :Springer Nature Singapore :2022. - ix, 139 p. :ill., digital ;24 cm. - Translational bioinformatics,v. 182213-2783 ;. - Translational bioinformatics ;v. 18..

Chapter 1. Introduction to multi-omics data and analytical methods for TCM and diseases -- Chapter 2. TCM and diseases: the needs for multi-omics -- Chapter 3. TCM related multi-omics data integration techniques -- Chapter 4. TCM related multi-omics data mining techniques -- Chapter 5. TCM preparation quality control: biological and chemical ingredient analysis -- Chapter 6. TCM preparation source tracking -- Chapter 7. TCM preparation network pharmacology analysis -- Chapter 8. TCM analysis data resources, web services and visualizations -- Chapter 9. TCM geoherbalism examination and authentic TCM identification.

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.

ISBN: 9789811947711

Standard No.: 10.1007/978-981-19-4771-1doiSubjects--Topical Terms:

554962
Medicine, Chinese.

LC Class. No.: R602 / .T73 2022

Dewey Class. No.: 610.951

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