Systems biomedicine approaches in cancer research / Shailza Singh, editor.
Singh, Shailza, editor.
2022
RC269.7
Available Online

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Title Systems biomedicine approaches in cancer research / Shailza Singh, editor.
ISBN 9789811919534 (electronic bk.)
9811919534 (electronic bk.)
9789811919527
9811919526
DOI https://doi.org/10.1007/978-981-19-1953-4
Published Singapore : Springer, [2022]
Copyright ©2022
Language English
Description 1 online resource (xi, 163 pages)
Other Standard Identifiers 10.1007/978-981-19-1953-4 doi
Call Number RC269.7
Dewey Decimal Classification 616.99/407
Summary This book presents the applications of systems biology and synthetic biology in cancer medicine. It highlights the use of computational and mathematical models to decipher the complexity of cancer heterogeneity. The book emphasizes the modeling approaches for predicting behavior of cancer cells, tissues in context of drug response, and angiogenesis. It introduces cell-based therapies for the treatment of various cancers and reviews the role of neural networks for drug response prediction. Further, it examines the system biology approaches for the identification of medicinal plants in cancer drug discovery. It explores the opportunities for metabolic engineering in the realm of cancer research towards development of new cancer therapies based on metabolically derived targets. Lastly, it discusses the applications of data mining techniques in cancer research. This book is an excellent guide for oncologists and researchers who are involved in the latest cancer research.
Access Note Access limited to authorized users.
Source of Description Online resource; title from PDF title page (SpringerLink, viewed August 22, 2022).
Added Author Singh, Shailza, editor.
Available in Other Form Print version: 9789811919527
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Chapter 1_Systems Complexity in Cancer
Chapter 2_Engineered Biotherapeutics through Synthetic Biology in Cancer
Chapter 3_Cancer Immunotherapy: A Potential Convergence between Systems and Synthetic Biology
Chapter 4_Cell Based Therapeutic Devices in Cancer
Chapter 5_Case Studies on Medicinal Plants in Cancer Drug Discovery using System Approaches
Chapter 7_Metabolic engineering and synthetic biology devices in treating Cancer
Chapter 8_Cancer Biomarkers in the era of Systems Biology
Chapter 9_Supervised vs Non-Supervised Learning to Combat Cancer
Chapter 10_Designing Cancer Biological Systems using Synthetic Engineering
Chapter 11_Biosystems and Genetic Engineering Tools in Cancer Theranostics
Chapter 12_Role of HPC in Cancer Informatics
Chapter 13_Statistical ML for Cancer Therapeutics
Chapter 14_Data Mining and Knowledge Discovery in Cancer
Chapter 15_TCGA Data from TensorFlow Optimization.

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