V. samuel raj (39 résultats)

Paperback or Softback. Etat : New. Artificial Intelligence in Managing Antimicrobial Resistance. Book.

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Etat : New. Ramendra Pati Pandey is a professor in the department of Biotechnology and Microbiology at SRM University Delhi-NCR, Sonepat, Haryana, India. He has been appointed as an Ambassador of the Royal Society of Biology, London, UK (2023). He w.

Etat : As New. Unread book in perfect condition.

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Etat : As New. Unread book in perfect condition.

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Etat : As New. Unread book in perfect condition.

Etat : New. In English.

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Hardback. Etat : New. This volume reviews the use of machine learning (ML) to predict antibiotic resistance in pathogens based on gene content and genome composition as data sets comprising hundreds or thousands of pathogen genomes become available. One of the main goals of this work is to promote the use of ML in front-line contexts while simultaneously emphasizing the additional improvements that are required to use these techniques in a secure and confident manner. Given the variety of quantitative and qualitative laboratory indicators of AMR, the issue of what to anticipate is not an easy one.This book is intended for academia, students of medical science, microbiology, biology, and biotechnology, as well as experts and scientists working in the fields of infectious diseases, government health organizations, and medicine.…

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Hardcover. Etat : Brand New. 170 pages. 8.50x5.43x8.74 inches. In Stock.

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Hardback. Etat : New. This volume reviews the use of machine learning (ML) to predict antibiotic resistance in pathogens based on gene content and genome composition as data sets comprising hundreds or thousands of pathogen genomes become available. One of the main goals of this work is to promote the use of ML in front-line contexts while simultaneously emphasizing the additional improvements that are required to use these techniques in a secure and confident manner. Given the variety of quantitative and qualitative laboratory indicators of AMR, the issue of what to anticipate is not an easy one.This book is intended for academia, students of medical science, microbiology, biology, and biotechnology, as well as experts and scientists working in the fields of infectious diseases, government health organizations, and medicine.…

Hardcover. Etat : Brand New. 424 pages. 9.18x6.12x9.21 inches. In Stock.

PAP. Etat : New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

Paperback. Etat : new. Paperback. This volume reviews the use of machine learning (ML) to predict antibiotic resistance in pathogens based on gene content and genome composition as data sets comprising hundreds or thousands of pathogen genomes become available. One of the main goals of this work is to promote the use of ML in front-line contexts while simultaneously emphasizing the additional improvements that are required to use these techniques in a secure and confident manner. Given the variety of quantitative and qualitative laboratory indicators of AMR, the issue of what to anticipate is not an easy one.This book is intended for academia, students of medical science, microbiology, biology, and biotechnology, as well as experts and scientists working in the fields of infectious diseases, government health organizations, and medicine. The book is intended for academia, student of medical science, microbiology, biology, biotechnology. Furthermore, infectious diseases experts, scientists working in the field of infectious diseases, government health organizations, and medical. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

PAP. Etat : New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

Paperback. Etat : new. Paperback. This volume reviews the use of machine learning (ML) to predict antibiotic resistance in pathogens based on gene content and genome composition as data sets comprising hundreds or thousands of pathogen genomes become available. One of the main goals of this work is to promote the use of ML in front-line contexts while simultaneously emphasizing the additional improvements that are required to use these techniques in a secure and confident manner. Given the variety of quantitative and qualitative laboratory indicators of AMR, the issue of what to anticipate is not an easy one.This book is intended for academia, students of medical science, microbiology, biology, and biotechnology, as well as experts and scientists working in the fields of infectious diseases, government health organizations, and medicine. The book is intended for academia, student of medical science, microbiology, biology, biotechnology. Furthermore, infectious diseases experts, scientists working in the field of infectious diseases, government health organizations, and medical. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.…

Paperback. Etat : new. Paperback. This volume reviews the use of machine learning (ML) to predict antibiotic resistance in pathogens based on gene content and genome composition as data sets comprising hundreds or thousands of pathogen genomes become available. One of the main goals of this work is to promote the use of ML in front-line contexts while simultaneously emphasizing the additional improvements that are required to use these techniques in a secure and confident manner. Given the variety of quantitative and qualitative laboratory indicators of AMR, the issue of what to anticipate is not an easy one.This book is intended for academia, students of medical science, microbiology, biology, and biotechnology, as well as experts and scientists working in the fields of infectious diseases, government health organizations, and medicine. The book is intended for academia, student of medical science, microbiology, biology, biotechnology. Furthermore, infectious diseases experts, scientists working in the field of infectious diseases, government health organizations, and medical. This item is printed on demand. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.…

Hardcover. Etat : new. Hardcover. This volume reviews the use of machine learning (ML) to predict antibiotic resistance in pathogens based on gene content and genome composition as data sets comprising hundreds or thousands of pathogen genomes become available. One of the main goals of this work is to promote the use of ML in front-line contexts while simultaneously emphasizing the additional improvements that are required to use these techniques in a secure and confident manner. Given the variety of quantitative and qualitative laboratory indicators of AMR, the issue of what to anticipate is not an easy one.This book is intended for academia, students of medical science, microbiology, biology, and biotechnology, as well as experts and scientists working in the fields of infectious diseases, government health organizations, and medicine. The book is intended for academia, student of medical science, microbiology, biology, biotechnology. Furthermore, infectious diseases experts, scientists working in the field of infectious diseases, government health organizations, and medical. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.…