國立虎尾科技大學 |

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

紀錄類型:	書目-語言資料,印刷品 : 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.;
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:

1462235
Reproductive health
--Data processing.

LC Class. No.: RG103

Dewey Class. No.: 618.1

Data-driven reproductive health = role of bioinformatics and machine learning methods /
LDR:02347nam a2200325 a 4500 001 1138493
003 DE-He213
005 20241018125720.0
006 m d
007 cr nn 008maaau
008 250117s2024 si s 0 eng d
020 $a 9789819774517 $q (electronic bk.)
020 $a 9789819774500 $q (paper)
024 7 $a 10.1007/978-981-97-7451-7 $2 doi
035 $a 978-981-97-7451-7
040 $a GP $c GP
041 0 $a eng
050 4 $a RG103
072 7 $a MFKC $2 bicssc
072 7 $a MED082000 $2 bisacsh
072 7 $a MFKC $2 thema
082 0 4 $a 618.1 $2 23
090 $a RG103 $b .D232 2024
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 : $c 2024. $b Springer Nature Singapore : $b Imprint: Springer,
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 1462235
650 1 4 $a Reproductive Medicine. $3 669081
650 2 4 $a Gynecology. $3 644094
650 2 4 $a Midwifery. $3 635850
650 2 4 $a Bioinformatics. $3 583857
650 2 4 $a Computational Intelligence. $3 768837
650 2 4 $a Artificial Intelligence. $3 646849
700 1 $a Sengupta, Abhishek. $3 1462234
710 2 $a SpringerLink (Online service) $3 593884
773 0 $t Springer Nature eBook
856 4 0 $u https://doi.org/10.1007/978-981-97-7451-7
950 $a Medicine (SpringerNature-11650)