Data-Driven Reproductive Health: Role of Bioinformatics and Machine Learning Methods

Sengupta, Abhishek; Narad, Priyanka; Majumdar, Gaurav; Modi, Deepak

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.

Details

Published by: Springer

Publication Date: 2024-10-18

Format: Hardcover

ISBN-13: 9789819774500

DOI: 10.1007/978-981-97-7451-7

Dimensions: 235.0cm x155.0cm

Pages: 231.0