Systems Biology and Machine Learning Methods in Reproductive Health - Sengupta, Abhishek; Narad, Priyanka; Gupta, Dinesh;(ed.) - Prospero Internet Bookshop
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Systems Biology and Machine Learning Methods in Reproductive Health

 
Edition number: 1
Publisher: Chapman and Hall
Date of Publication:
 
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GBP 120.00
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Short description:

Bringing science and data science together, this ground-breaking book provides scientists, clinicians, and students with a step-by-step guide to uncovering the complexities of reproductive health through cutting-edge computational tools.


 

Long description:
Systems Biology and Machine Learning Methods in Reproductive Health is an innovative and wide-ranging book that discovers the synergetic combination of disciplines: systems biology and machine learning, with an application in the field of reproductive health. This book assembles the expertise of leading scientists and clinicians to present a compilation of cutting-edge techniques and case studies utilizing computational methods to elucidate intricate biological systems, elucidate reproductive pathways, and address critical issues in the fields of fertility, pregnancy, and reproductive disorders. Bringing science and data science together, this groundbreaking book provides scientists, clinicians, and students with a step-by-step guide to uncovering the complexities of reproductive health through cutting-edge computational tools.
Table of Contents:

1.     Introduction to Systems Biology and Machine Learning                                         



2.     Data Sources and Data Integration in Reproductive Health                                  


3.     Genomics and Transcriptomics in Reproductive Health                                         


4.     Proteomics and Metabolomics in Reproductive Health                                    


5.     Systems Biology Approaches in Reproductive Health                                             



6.     Machine Learning Algorithms in Reproductive Health                                          



7.     Personalized Medicine in Reproductive Health                                                       



8.     Ethical and Privacy Considerations                                                                          



9.     Challenges and Future Directions