Data-Driven Reproductive Health
Role of Bioinformatics and Machine Learning Methods
Buch in deiner Nähe kaufen
Beschreibung
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.
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.
Covers the theoretical foundations of bioinformatics and machine learning in reproductive health Includes step-by-step tutorials on applying data mining techniques to reproductive data Provides case studies highlighting successful applications of data mining in reproductive medicine
Autor*in
Abhishek Sengupta
Themen in »Data-Driven Reproductive Health«
Computational Intelligence in Reproductive Health Artificial Intelligence in Reproductive Health Machine Learning in Reproductive Health Data Mining in Reproductive Health Predictive Modeling in Reproductive Health Personalized Medicine in Reproductive Health Population Studies in Reproductive Health Data-driven Healthcare in Reproductive Health Natural Language Processing (NLP) in Reproductive Health Time Series Analysis in Reproductive Health Bioinformatics and Multi-Omics Approaches in Reproductive Health