This book covers all aspects of computational biology in studying cancer diagnosis and prognosis, including newer applications involving infection and inflammation, as well as basic information on advanced simulation techniques. It describes the different tools, risk-based modeling techniques, early prediction algorithms and the biomarkers of different cancers that help in their early and better diagnosis in routine clinical practice involving multiple organs and systems. Early cancer diagnosis and artificial intelligence (AI) are rapidly evolving fields, with the UK's National Health…mehr
This book covers all aspects of computational biology in studying cancer diagnosis and prognosis, including newer applications involving infection and inflammation, as well as basic information on advanced simulation techniques. It describes the different tools, risk-based modeling techniques, early prediction algorithms and the biomarkers of different cancers that help in their early and better diagnosis in routine clinical practice involving multiple organs and systems.
Early cancer diagnosis and artificial intelligence (AI) are rapidly evolving fields, with the UK's National Health Service aiming to improve early diagnosis rates to 75% by 2028. Screening can improve early cancer detection and mortality, but patient selection and risk stratification are key challenges. AI algorithms can facilitate cancer diagnosis by triggering investigation in screened individuals according to clinical parameters and automating clinical workflows where capacity is limited. Machine learning, which learns complex data patterns to make predictions has the potential to revolutionize early cancer diagnosis and support capacity concerns through automation.
The chapters present the advances in diagnosing different types of cancer including bladder cancer, breast cancer, colorectal cancer, kidney (renal cell) cancer, lung cancer, lymphoma, pancreatic cancer, prostate cancer, skin cancer, uterine and metastatic cancers. The chapters also cover recurrent cancer, advanced cancer treatment, and the management of cancer in adolescents and young adults. The pan-cancer analyses presented in the book cover all aspects of early diagnosis, supplemented by numerous illustrations and figures to offer a fresh perspective and lucid understanding of computer-based approaches in cancer management.
This book simplifies computational methods in medical diagnosis and highlights the benefits of early detection compared to other methods. It is targeted at biomedical scientists and clinical practitioners who conduct artificial intelligence-based research.
Dr. Rajeev Nema is currently working as an assistant professor at Manipal University in Jaipur, Rajasthan. He has more than 9 years of post-doctoral experience focused on diverse molecular cancer biology studies. Barkatullah University's Life Science Department awarded him a Ph.D. He used natural product chemistry in his thesis study to define the key chemical activities of three distinct cancer cell lines: A549 (lung cancer), MCF-7 (breast cancer), and PA-1 (pancreatic cancer). He worked as a Research Associate at the All-India Institute of Medical Science (AIIMS) in Bhopal in the Department of Biochemistry to investigate the "crosstalk between ROR1/2 and S1P signaling" pathways and how these pathways regulate cell growth, apoptosis, and invasion in lung cancer cells. Following that he worked as a Senior Research Scientist in the Department of Gene Expression (Oncology Development) at 3B BlackBio Biotech India Ltd., 7-C, Industrial Area, Govindpura, Bhopal, 462023, (M.P.) India. In addition, from February to August 2023, he worked as a postdoctoral researcher in the Department of Oncological Sciences at Mount Sinai's Icahn School of Medicine. Dr. Ashok Kumar is currently working as an Professor in the Department of Biochemistry of All India Institute of Medical Sciences Bhopal. His current research interest is understanding the role of non-coding RNAs and sphingosine-1-phosphate signaling in Head and Neck Cancer. He received his PhD degree from Sanjay Gandhi Postgraduate Institute of Medical Sciences (SGPGIMS) Lucknow, India. Then, he received postdoctoral training from UCSF-Benioff’s Children’s Hospital Oakland & Research Centre, Oakland, CA, USA. Dr. Ashok Kumar’s major area of research work is Cell & Molecular Biology, Cancer Biology, Immunology & Sphingolipid signaling. He has more than 15 years of research experience. He has published more than 50 research articles and 10 book chapters, and he has edited two books. He is a member of several international professional societies including American Association of Cancer Research and Fellow of Royal Society of Biology. Prof. Dinesh Kumar Saini is currently working as a professor in the department of computer and communication engineering at the School of Computers and IT at Manipal University Jaipur, Jaipur, India. His current research interests are computer science and engineering, information technology, health science and AI, software systems, business administration, and human resource management. He received his PhD degree from Birla Institute of Technology and Science, Pilani, Rajasthan, India. While serving as Dean of the Faculty of Computing and Information Technology in Oman, he maintained a strong academic record. He has administrative experience as a university senator, university research council member, university library committee member, faculty research coordinator, etc. He has conceived, planned, developed, organized, and implemented numerous seminars, conferences, MDPs, EDPs, and FDPs. He has published more than 140 research articles and 10 book chapters, as well as edited two books. He is a member of several international professional societies, including FIMA, FRSS, FIANG, MIEEE, MACM, and MCSI.
Inhaltsangabe
.- Chapter 1: Cancer Diagnosis: An overview .- Chapter 2: Computational Methods in Oncology .- Chapter 3: Overview of Computational Approaches for Cancer Diagnosis .- Chapter 4: Artificial intelligence and Machine Learning in improving diagnostic accuracy .- Chapter 5: Advances in Image Processing and Pattern Recognition .- Chapter 6: Genomics and Bioinformatics in the discovery and validation of diagnostic biomarkers .- Chapter 7: Computer-based wearable devices for remote patient monitoring. .- Chapter 8: Overview of computational approaches for Cancer Prognosis .- Chapter 9: Predictive Modeling for Cancer Prognosis .- Chapter 10:Genomics and Transcriptomics based predictive and prognostic biomarkers .- Chapter 11:Artificial Intelligence in Personalized Medicine and Treatment Planning .- Chapter 12:Integrative Multi-Omics Approaches .- Chapter 13:Challenges and Limitations of Computational Methods in Oncology .- Chapter 14:Challenges in Integration of computational approaches with Clinical Practice .- Chapter 15:Emerging Technologies in Computational Oncology .- Chapter 16:Statement On the Effectiveness of AI And ML In Cancer Care .- Chapter 17:Future Directions in Computational Cancer Research .- Chapter 18:Deep learning algorithms to analyze medical images for early detection of cancer .- Chapter 19:Ethical considerations regarding patient privacy and data security .- Chapter 20:Call for further research and implementation of deep learning technologies in oncology for enhanced healthcare delivery.
.- Chapter 1: Cancer Diagnosis: An overview .- Chapter 2: Computational Methods in Oncology .- Chapter 3: Overview of Computational Approaches for Cancer Diagnosis .- Chapter 4: Artificial intelligence and Machine Learning in improving diagnostic accuracy .- Chapter 5: Advances in Image Processing and Pattern Recognition .- Chapter 6: Genomics and Bioinformatics in the discovery and validation of diagnostic biomarkers .- Chapter 7: Computer-based wearable devices for remote patient monitoring. .- Chapter 8: Overview of computational approaches for Cancer Prognosis .- Chapter 9: Predictive Modeling for Cancer Prognosis .- Chapter 10:Genomics and Transcriptomics based predictive and prognostic biomarkers .- Chapter 11:Artificial Intelligence in Personalized Medicine and Treatment Planning .- Chapter 12:Integrative Multi-Omics Approaches .- Chapter 13:Challenges and Limitations of Computational Methods in Oncology .- Chapter 14:Challenges in Integration of computational approaches with Clinical Practice .- Chapter 15:Emerging Technologies in Computational Oncology .- Chapter 16:Statement On the Effectiveness of AI And ML In Cancer Care .- Chapter 17:Future Directions in Computational Cancer Research .- Chapter 18:Deep learning algorithms to analyze medical images for early detection of cancer .- Chapter 19:Ethical considerations regarding patient privacy and data security .- Chapter 20:Call for further research and implementation of deep learning technologies in oncology for enhanced healthcare delivery.
Es gelten unsere Allgemeinen Geschäftsbedingungen: www.buecher.de/agb
Impressum
www.buecher.de ist ein Internetauftritt der buecher.de internetstores GmbH
Geschäftsführung: Monica Sawhney | Roland Kölbl | Günter Hilger
Sitz der Gesellschaft: Batheyer Straße 115 - 117, 58099 Hagen
Postanschrift: Bürgermeister-Wegele-Str. 12, 86167 Augsburg
Amtsgericht Hagen HRB 13257
Steuernummer: 321/5800/1497
USt-IdNr: DE450055826