TY  - BOOK
AU  - Sengupta,Abhishek
AU  - Narad,Priyanka
AU  - Majumdar,Gaurav
AU  - Modi,Deepak
ED  - SpringerLink (Online service)
TI  - Data-Driven Reproductive Health: Role of Bioinformatics and Machine Learning Methods 
SN  - 9789819774517
AV  - R1 
PY  - 2024///
CY  - Singapore
PB  - Springer Nature Singapore, Imprint: Springer
KW  - Reproductive health
KW  - Gynecology 
KW  - Midwifery
KW  - Bioinformatics
KW  - Computational intelligence
KW  - Artificial intelligence
KW  - Reproductive Medicine
KW  - Gynecology
KW  - Computational Intelligence
KW  - Artificial Intelligence
N1  - 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
N2  - 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
UR  - https://doi.org/10.1007/978-981-97-7451-7
ER  -