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001448716 001__ 1448716
001448716 003__ OCoLC
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001448716 019__ $$a1341442967
001448716 020__ $$a9789811919534$$q(electronic bk.)
001448716 020__ $$a9811919534$$q(electronic bk.)
001448716 020__ $$z9789811919527
001448716 020__ $$z9811919526
001448716 0247_ $$a10.1007/978-981-19-1953-4$$2doi
001448716 035__ $$aSP(OCoLC)1340947213
001448716 040__ $$aYDX$$beng$$erda$$epn$$cYDX$$dGW5XE$$dEBLCP$$dOCLCF$$dOCLCQ
001448716 049__ $$aISEA
001448716 050_4 $$aRC269.7
001448716 08204 $$a616.99/407$$223/eng/20220822
001448716 24500 $$aSystems biomedicine approaches in cancer research /$$cShailza Singh, editor.
001448716 264_1 $$aSingapore :$$bSpringer,$$c[2022]
001448716 264_4 $$c©2022
001448716 300__ $$a1 online resource (xi, 163 pages)
001448716 336__ $$atext$$btxt$$2rdacontent
001448716 337__ $$acomputer$$bc$$2rdamedia
001448716 338__ $$aonline resource$$bcr$$2rdacarrier
001448716 5050_ $$aChapter 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.
001448716 506__ $$aAccess limited to authorized users.
001448716 520__ $$aThis 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.
001448716 588__ $$aOnline resource; title from PDF title page (SpringerLink, viewed August 22, 2022).
001448716 650_0 $$aCancer cells$$xProliferation$$xMathematical models.
001448716 650_0 $$aCancer$$xTreatment.
001448716 650_0 $$aBiometry.
001448716 655_0 $$aElectronic books.
001448716 7001_ $$aSingh, Shailza,$$eeditor.
001448716 77608 $$iPrint version: $$z9811919526$$z9789811919527$$w(OCoLC)1302740892
001448716 852__ $$bebk
001448716 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-981-19-1953-4$$zOnline Access$$91397441.1
001448716 909CO $$ooai:library.usi.edu:1448716$$pGLOBAL_SET
001448716 980__ $$aBIB
001448716 980__ $$aEBOOK
001448716 982__ $$aEbook
001448716 983__ $$aOnline
001448716 994__ $$a92$$bISE