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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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 was awarded the NESA Scientist of the Year Award 2023 by the National Environmental Science Academy, India, and selected for the Shri Om Prakash Sharma Award from the Indian Academy of Biomedical Sciences 2023 for his outstanding contributions in biomedical sciences, especially in basic translational research, and the Polyfeenolix-IIS Best Mid-Career Immunology Teacher Award 2023 in the 50th IMMUNOCON in October 2023.
Chung-Ming Chang has a Ph.D. degree in Animal Genetics from Institute National Agronomique Paris-Grignon, France, and a Master's in Veterinary Medicine from National Taiwan University, Taiwan. He is also a Doctor of Veterinary Medicine, National License of Doctor Veterinary Medicine, Taiwan. He has expertise in virology and human molecular genetics; avian influenza; human endogenous retrovirus: copy number and insertional polymorphism study of human endogenous retrovirus HERV-K in rheumatoid arthritis patients. He has published several articles and review papers in international journals of high repute and also presented a dozen papers in national and international symposia/conferences.
V. Samuel Raj is a Professor of Microbiology and Biotechnology and Director of the Centre for Drug Design Discovery & Development (C4D) at SRM University Delhi-NCR, Sonepat, Haryana, India, since 2014. Prior to that he had 10 years of R&D experience with 2 major pharma companies and 15 years of research experience in Taiwan, Japan, and the USA. He has experience as Group Leader of Microbiology at Ranbaxy Research Laboratories and has led many drug discovery projects against infectious diseases. Before joining Ranbaxy, he was a visiting scientist (faculty) in the Department of Microbiology, School of Medicine, University of Pennsylvania, Philadelphia, USA.
Les informations fournies dans la section « A propos du livre » peuvent faire référence à une autre édition de ce titre.
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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. N° de réf. du vendeur 9781032812458
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