000			03432nam a22004695i 4500
001			978-981-19-1953-4
003			DE-He213
005			20240821192814.0
007			cr nn 008mamaa
008			220810s2022 si | s |||| 0|eng d
020			_a9789811919534 _9978-981-19-1953-4
024	7		_a10.1007/978-981-19-1953-4 _2doi
072		7	_aPSB _2bicssc
072		7	_aSCI007000 _2bisacsh
072		7	_aPSE _2thema
245	1	0	_aSystems Biomedicine Approaches in Cancer Research
250			_a1st ed. 2022.
264		1	_aSingapore : _bSpringer Nature Singapore : _bImprint: Springer, _c2022.
300			_aXI, 163 p. 1 illus. _bonline resource.
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
347			_atext file _bPDF _2rda
505	0		_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.
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.
650		0	_aSynthetic Biology.
650		0	_aCancer.
650		0	_aBioinformatics.
650		0	_aCancer _xTreatment.
650	1	4	_aSynthetic Biology.
650	2	4	_aCancer Biology.
650	2	4	_aComputational and Systems Biology.
650	2	4	_aCancer Therapy.
700	1		_aSingh, Shailza. _eeditor. _4edt _4http://id.loc.gov/vocabulary/relators/edt
710	2		_aSpringerLink (Online service)
856			_u#gotoholdings _yAccess resource
912			_aZDB-2-SBL
912			_aZDB-2-SXB
245			_h[E-Book]
999			_c100893 _d100893