LEADER 04126nam 22007215 450 001 9910586583603321 005 20240527103503.0 010 $a9789811919534$b(electronic bk.) 010 $z9789811919527 024 7 $a10.1007/978-981-19-1953-4 035 $a(MiAaPQ)EBC7072651 035 $a(Au-PeEL)EBL7072651 035 $a(CKB)24368768300041 035 $a(DE-He213)978-981-19-1953-4 035 $a(PPN)264196104 035 $a(EXLCZ)9924368768300041 100 $a20220810d2022 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aSystems Biomedicine Approaches in Cancer Research /$fedited by Shailza Singh 205 $a1st ed. 2022. 210 1$aSingapore :$cSpringer Nature Singapore :$cImprint: Springer,$d2022. 215 $a1 online resource (170 pages) 311 08$aPrint version: Singh, Shailza Systems Biomedicine Approaches in Cancer Research Singapore : Springer,c2022 9789811919527 320 $aIncludes bibliographical references. 327 $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. 330 $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. 606 $aSynthetic Biology 606 $aCancer 606 $aBioinformatics 606 $aCancer$xTreatment 606 $aSynthetic Biology 606 $aCancer Biology 606 $aComputational and Systems Biology 606 $aCancer Therapy 606 $aCāncer$2thub 606 $aBiometria$2thub 606 $aProliferaciķ celˇlular$2thub 606 $aTerapčutica$2thub 608 $aLlibres electrōnics$2thub 615 0$aSynthetic Biology. 615 0$aCancer. 615 0$aBioinformatics. 615 0$aCancer$xTreatment. 615 14$aSynthetic Biology. 615 24$aCancer Biology. 615 24$aComputational and Systems Biology. 615 24$aCancer Therapy. 615 7$aCāncer 615 7$aBiometria 615 7$aProliferaciķ celˇlular 615 7$aTerapčutica 676 $a570.15195 702 $aSingh$b Shailza 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 912 $a9910586583603321 996 $aSystems Biomedicine Approaches in Cancer Research$92905330 997 $aUNINA