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Titlebook: Data-Driven Reproductive Health; Role of Bioinformati Abhishek Sengupta,Priyanka Narad,Deepak Modi Book 2024 The Editor(s) (if applicable)

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發(fā)表于 2025-3-21 20:03:15 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
書(shū)目名稱Data-Driven Reproductive Health
副標(biāo)題Role of Bioinformati
編輯Abhishek Sengupta,Priyanka Narad,Deepak Modi
視頻videohttp://file.papertrans.cn/285/284465/284465.mp4
概述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 s
圖書(shū)封面Titlebook: Data-Driven Reproductive Health; Role of Bioinformati Abhishek Sengupta,Priyanka Narad,Deepak Modi Book 2024 The Editor(s) (if applicable)
描述.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..
出版日期Book 2024
關(guān)鍵詞Computational Intelligence in Reproductive Health; Artificial Intelligence in Reproductive Health; Mac
版次1
doihttps://doi.org/10.1007/978-981-97-7451-7
isbn_softcover978-981-97-7453-1
isbn_ebook978-981-97-7451-7
copyrightThe Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapor
The information of publication is updating

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https://doi.org/10.1007/978-3-531-90990-5of the key highlights of the chapter is the emphasis on the ethical considerations of aspects of data collection, storage, and sharing, emphasizing the significance of privacy and informed consent in preserving the integrity of sensitive reproductive health information. In the last section, the chap
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extension to the centralized data hub, a classic example of the ArogyaSetu mobile application during COVID-19 is discussed in detail as a case study. The next section talks about “Why we need an ocean of data in reproductive healthcare considering the challenges and ethical aspects of it.” Further i
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within a tube. Furthermore, we showcase an example application of leveraging SVR to create a virtual screening prediction tool that highlights its strength in handling both linear and nonlinear regression problems. Overall, the chapter serves as a valuable guide to SVR, emphasizing its capacity to
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d guidelines, presenting a holistic view of the evolving ethical standards. We will examine the specific regulations and guidelines applicable to this field, helping readers grasp the changing ethical landscape. Real-world case studies will illustrate our insights, shedding light on the ethical impl
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