Machine Learning in Single-Cell RNA-seq Data Analysis (Paperback)

Khalid Raza

ISBN 10: 9819767024 / ISBN 13: 9789819767021

Editore: Springer Verlag, Singapore, 2024

Riguardo questo articolo

Paperback. This book provides a concise guide tailored for researchers, bioinformaticians, and enthusiasts eager to unravel the mysteries hidden within single-cell RNA sequencing (scRNA-seq) data using cutting-edge machine learning techniques. The advent of scRNA-seq technology has revolutionized our understanding of cellular diversity and function, offering unprecedented insights into the intricate tapestry of gene expression at the single-cell level. However, the deluge of data generated by these experiments presents a formidable challenge, demanding advanced analytical tools, methodologies, and skills for meaningful interpretation. This book bridges the gap between traditional bioinformatics and the evolving landscape of machine learning. Authored by seasoned experts at the intersection of genomics and artificial intelligence, this book serves as a roadmap for leveraging machine learning algorithms to extract meaningful patterns and uncover hidden biological insights within scRNA-seq datasets. This book provides a concise guide tailored for researchers, bioinformaticians, and enthusiasts eager to unravel the mysteries hidden within single-cell RNA sequencing (scRNA-seq) data using cutting-edge machine learning techniques. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

Codice articolo 9789819767021

Dati bibliografici

• Titolo: Machine Learning in Single-Cell RNA-seq Data Analysis (Paperback)
• Editore: Springer Verlag, Singapore
• Data di pubblicazione: 2024
• Lingua: Inglese
• ISBN 10: 9819767024
• ISBN 13: 9789819767021
• Rilegatura: Paperback
• Condizione: new

Descrizione articolo

Riassunto

This book provides a concise guide tailored for researchers, bioinformaticians, and enthusiasts eager to unravel the mysteries hidden within single-cell RNA sequencing (scRNA-seq) data using cutting-edge machine learning techniques. The advent of scRNA-seq technology has revolutionized our understanding of cellular diversity and function, offering unprecedented insights into the intricate tapestry of gene expression at the single-cell level. However, the deluge of data generated by these experiments presents a formidable challenge, demanding advanced analytical tools, methodologies, and skills for meaningful interpretation. This book bridges the gap between traditional bioinformatics and the evolving landscape of machine learning. Authored by seasoned experts at the intersection of genomics and artificial intelligence, this book serves as a roadmap for leveraging machine learning algorithms to extract meaningful patterns and uncover hidden biological insights within scRNA-seq datasets. 

Informazioni sull?autore

Dr. Khalid Raza is an Associate Professor at the Department of Computer Science, Jamia Millia Islamia (Central University), New Delhi, India. He also served as a ICCR Chair Visiting Professor at the Faculty of Computer & Information Sciences, Ain Shams University, Cairo, Egypt academic session 2017-2018. Dr. Raza has over 13 years of teaching and research experience at undergraduate, postgraduate and doctoral levels. He has contributed over 140 peer-reviewed articles in refereed international journals, conference proceedings, and as book chapters. Dr. Raza has authored/edited 10 Books published by reputed publishers like Springer-Nature, Elsevier, and CRC Press. Dr. Raza is an Academic Editor of PeerJ Computer Science International Journal, Guest Editor of the Natural Product Communications (SAGE), and Mini-Reviews in Medicinal Chemistry (Bentham Science). He has an active collaboration with the scientists from leading institutions of India and abroad. He is an Honorary Research Fellow of INIT International University, Malaysia. Dr. Raza has been featured in the list of World's Top 2% Scientists released by Stanford University (USA) in collaboration with Elsevier for the two consecutive years 2021 and 2022.