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ISBN 10: 1032783702 ISBN 13: 9781032783703
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ISBN 10: 1032783702 ISBN 13: 9781032783703
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Published by Chapman and Hall/CRC, 2024
ISBN 10: 1032783702 ISBN 13: 9781032783703
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Published by Chapman and Hall/CRC, 2024
ISBN 10: 1032783702 ISBN 13: 9781032783703
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Published by Chapman and Hall/CRC, 2024
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hardcover. Condition: Fine. Open Books is a nonprofit social venture that provides literacy experiences for thousands of readers each year through inspiring programs and creative capitalization of books.
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Published by Taylor & Francis Ltd, 2024
ISBN 10: 1032783702 ISBN 13: 9781032783703
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Published by Chapman and Hall/CRC, 2024
ISBN 10: 1032783702 ISBN 13: 9781032783703
Seller: Books Puddle, New York, NY, U.S.A.
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Published by Chapman and Hall/CRC, 2024
ISBN 10: 1032783702 ISBN 13: 9781032783703
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Published by Chapman and Hall/CRC, 2024
ISBN 10: 1032783702 ISBN 13: 9781032783703
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Published by Chapman and Hall/CRC, 2024
ISBN 10: 1032755512 ISBN 13: 9781032755519
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Published by Springer Verlag, Singapore, Singapore, 2024
ISBN 10: 9819774500 ISBN 13: 9789819774500
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Hardcover. Condition: new. Hardcover. 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. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
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Published by Chapman and Hall/CRC, 2024
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Published by Chapman and Hall/CRC, 2024
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ISBN 10: 1032755512 ISBN 13: 9781032755519
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Published by Chapman and Hall/CRC, 2024
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Published by Chapman and Hall/CRC, 2024
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Published by Taylor & Francis Ltd, 2024
ISBN 10: 1032755512 ISBN 13: 9781032755519
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Published by Chapman and Hall/CRC, 2024
ISBN 10: 1032755512 ISBN 13: 9781032755519
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Published by Chapman and Hall/CRC, 2024
ISBN 10: 1032755512 ISBN 13: 9781032755519
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Published by Springer Nature Singapore, Springer Nature Singapore Okt 2024, 2024
ISBN 10: 9819774500 ISBN 13: 9789819774500
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Buch. Condition: Neu. Neuware -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.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 244 pp. Englisch.
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Add to basketCondition: Brand New. Brand New! Fast Delivery This is an International Edition and ship within 24-48 hours. Deliver by FedEx and Dhl, & Aramex, UPS, & USPS and we do accept APO and PO BOX Addresses. Order can be delivered worldwide within 7-12 days and we do have flat rate for up to 2LB. Extra shipping charges will be requested if the Book weight is more than 5 LB. This Item May be shipped from India, United states & United Kingdom. Depending on your location and availability.
Language: English
Published by Springer Nature Singapore, Springer Nature Singapore, 2024
ISBN 10: 9819774500 ISBN 13: 9789819774500
Seller: AHA-BUCH GmbH, Einbeck, Germany
Buch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - 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.