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

Record Type:	Electronic resources : Monograph/item
Title/Author:	Data-driven reproductive health/ edited by Abhishek Sengupta ... [et al.].
Reminder of title:	role of bioinformatics and machine learning methods /
other author:	Sengupta, Abhishek.
Published:	Singapore :Springer Nature Singapore : : 2024.,
Description:	xi, 231 p. :ill. (chiefly color), digital ;24 cm.
[NT 15003449]:	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
Subject:	Reproductive health - Data processing. -
Online resource:	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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245 0 0 $a Data-driven reproductive health $h [electronic resource] : $b role of bioinformatics and machine learning methods / $c edited by Abhishek Sengupta ... [et al.].
260 $a Singapore : $b Springer Nature Singapore : $b Imprint: Springer, $c 2024.
300 $a xi, 231 p. : $b ill. (chiefly color), digital ; $c 24 cm.
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.
650 0 $a Reproductive health $x Data processing. $3 3733840
650 1 4 $a Reproductive Medicine. $3 874352
650 2 4 $a Gynecology. $3 618102
650 2 4 $a Midwifery. $3 795518
650 2 4 $a Bioinformatics. $3 553671
650 2 4 $a Computational Intelligence. $3 1001631
650 2 4 $a Artificial Intelligence. $3 769149
700 1 $a Sengupta, Abhishek. $3 3733839
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773 0 $t Springer Nature eBook
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