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Data-driven reproductive health = role of bioinformatics and machine learning methods /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Data-driven reproductive health/ edited by Abhishek Sengupta ... [et al.].
其他題名:	role of bioinformatics and machine learning methods /
其他作者:	Sengupta, Abhishek.
出版者:	Singapore :Springer Nature Singapore : : 2024.,
面頁冊數:	xi, 231 p. :ill. (chiefly color), digital ;24 cm.
內容註:	1 Introduction to Data Mining in Reproductive Health -- 2 Reproductive Health Data Sources -- 3 Pre-processing and Integration of Reproductive Health Data -- 4 Multi-omics Approaches for Reproductive Health Data -- 5 Association Rule Mining in Reproductive Health Data -- 6 Modeling in Reproductive Health and Treatment Outcomes -- 7 Clustering Analysis of Reproductive Health Data -- 8 Text Mining and NLP in Reproductive Health -- 9 Time Series Analysis in Reproductive Health Data -- 10 Data Mining Ethics in Reproductive Health -- 11 Reproductive Health Data Mining: Case Studies -- 12 Future Directions and Emerging Trends in Reproductive Health.
Contained By:	Springer Nature eBook
標題:	Reproductive health - Data processing. -
電子資源:	https://doi.org/10.1007/978-981-97-7451-7
ISBN:	9789819774517

Data-driven reproductive health = role of bioinformatics and machine learning methods /
Data-driven reproductive healthrole of bioinformatics and machine learning methods /[electronic resource] :edited by Abhishek Sengupta ... [et al.]. - Singapore :Springer Nature Singapore :2024. - xi, 231 p. :ill. (chiefly color), digital ;24 cm.

1 Introduction to Data Mining in Reproductive Health -- 2 Reproductive Health Data Sources -- 3 Pre-processing and Integration of Reproductive Health Data -- 4 Multi-omics Approaches for Reproductive Health Data -- 5 Association Rule Mining in Reproductive Health Data -- 6 Modeling in Reproductive Health and Treatment Outcomes -- 7 Clustering Analysis of Reproductive Health Data -- 8 Text Mining and NLP in Reproductive Health -- 9 Time Series Analysis in Reproductive Health Data -- 10 Data Mining Ethics in Reproductive Health -- 11 Reproductive Health Data Mining: Case Studies -- 12 Future Directions and Emerging Trends in Reproductive Health.

This book provides insight into the transformative impact of data-driven approaches on reproductive health. Chapters cover a wealth of intricate algorithms of genomic analysis, predictive modeling, and personalized treatment strategies, providing an up-to-date view of the reproductive healthcare landscape. With more than 20 code-based examples, the book decodes complex biological data using bioinformatics and machine learning and provides valuable insights into fertility, genetic disorders, and personalized medicine. This book is relevant for healthcare professionals, researchers, and students in the fields of reproductive medicine, bioinformatics, and genetics.

ISBN: 9789819774517

Standard No.: 10.1007/978-981-97-7451-7doiSubjects--Topical Terms:

3733840
Reproductive health
--Data processing.

LC Class. No.: RG103

Dewey Class. No.: 618.1

Data-driven reproductive health = role of bioinformatics and machine learning methods /
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505 0 $a 1 Introduction to Data Mining in Reproductive Health -- 2 Reproductive Health Data Sources -- 3 Pre-processing and Integration of Reproductive Health Data -- 4 Multi-omics Approaches for Reproductive Health Data -- 5 Association Rule Mining in Reproductive Health Data -- 6 Modeling in Reproductive Health and Treatment Outcomes -- 7 Clustering Analysis of Reproductive Health Data -- 8 Text Mining and NLP in Reproductive Health -- 9 Time Series Analysis in Reproductive Health Data -- 10 Data Mining Ethics in Reproductive Health -- 11 Reproductive Health Data Mining: Case Studies -- 12 Future Directions and Emerging Trends in Reproductive Health.
520 $a This book provides insight into the transformative impact of data-driven approaches on reproductive health. Chapters cover a wealth of intricate algorithms of genomic analysis, predictive modeling, and personalized treatment strategies, providing an up-to-date view of the reproductive healthcare landscape. With more than 20 code-based examples, the book decodes complex biological data using bioinformatics and machine learning and provides valuable insights into fertility, genetic disorders, and personalized medicine. This book is relevant for healthcare professionals, researchers, and students in the fields of reproductive medicine, bioinformatics, and genetics.
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