000			06214nam a22003137i 4500
005			20250903153534.0
008			250817s2023 flua|||ff|||| 00| 0 eng d
020			_a9781032398877
040			_aEG-GaU‬‬ _cEG-GaU‬‬ _dEG-GaU‬‬ _erda
082	0	4	_223 _a615.7 _bW.R.Q
100	1		_aWu, Rongling _eAUTHOR _963036
245	1	0	_aQuantitative methods for precision medicine : _bpharmacogenomics in action / _cRongling Wu
250			_a First edition.
264		1	_aBoca Raton : _bC&H/CRC Press, _c2023.
300			_axv, 289 pages : _bIllustrations (black and white); _c24 cm
336			_2rdacontent _atext _btxt
337			_2rdamedia _aunmediated _bn
338			_2rdacarrier _avolume _bnc
490			_aChapman & Hall/CRC biostatistics series
504			_aIncludes bibliographical references and index.
505			_a1. Methodological Foundation of Precision Medicine. 1.1. Interpersonal variability in drug response. 1.2. Mechanistic modelling of drug response. 1.3 Statistical models for mapping drug response. 1.4 Network mapping of drug response. 1.5 Conclusions and Outlook. Part I: Pharmacokinetic–Pharmacodynamic Pharmacogenetics. 2. Pharmacogenetic Dissection by Functional Mapping. 2.1. Introduction. 2.2. Quantitative Genetics. 2.3. A General Framework for Functional Mapping. 2.4 Pharmacogenetic Application of Functional Mapping. 2.5. High-dimensional Functional Mapping. 2.6. Concluding Remarks. 3. A Multiscale Model of Pharmacokinetic-Pharmacodynamic Mapping. 3.3. Heterochronopharmacodynamic Mapping. 3.4. Mapping Multifaceted Drug Reactions. 3.5. Concluding Remarks. 4. Pharmacogenetic Mapping of Missing Longitudinal Data. 4.1 Introduction. 4.2. Strategies for Modeling Non-Ignorable Dropout Data. 4.3. Haplotyping Drug Response Using the Pattern-Mixture Model. 4.4. Haplotyping Drug Response Using the Selection Model. 4.5. Concluding Remarks. 5. Systems Mapping of Drug Response. 5.1. Introduction. 5.2. ODE Modeling of PK/PD Machineries. 5.3. Systems Mapping: Model and Algorithm. 5.4. Stochastic Systems Mapping. 5.5. Concluding Remarks. Part II. Network Pharmacogenetics. 6. Network Mapping of Drug Response. 6.1. Introduction. 6.2. Functional Graph Theory. 6.3. Functional Pharmacogenetic Interaction Networks: An Example. 6.4. Fine-Grained Dissection of Pharmacogenetic Networks. 6.5. Modularity Theory and Dunbar’s law. 6.6. Concluding Remarks. 7. Learning Individualized Pharmacogenetic Networks. 7.1. Introduction. 7.2. A Framework for Network Inference. 7.3. Coalescing Individualized Networks into Stratification-Specific Networks. 7.4. Computer Simulation. 7.5. Reconstructing Multilayer Genetic Networks. 7.6. Concluding Remarks. 8. A Game-Theoretic Model of Cell Crosstalk in Drug Response. 8.1. Introduction. 8.2. GameTalker: A crosstalk model of tumor-microenvironment interactions. 8.3. Modeling Personalized Cell-Cell Interaction Networks. 8.4. Reconstructing multilayer gene regulatory networks of tumor-TME interactions. 8.5. Predictive network model for cancer growth. 8.6 Concluding Remarks. 9. A Graph Model of Personalized Drug-Drug Interactions. 9.1. Introduction. 9.2. Inferring DDI networks. 9.3. Inferring dynamic DDI networks from static data. 9.4. Coalescing high-order DDIs into hypernetworks. 9.5. Learning Large-scale DDI Networks. 9.6. Concluding remarks. 10. Pharmacogenomics as a Cornerstone of Precision Medicine: Methodological Leveraging. 10.1. Introduction. 10.2. How Drug Works. 10.3. Correcting for Relatedness in Pharmacogenomics GWAS. 10.4 Family-based Designs for PGx Studies. 10.5. Intertwined Epistatic and Epistatic Networks. 10.6. Pharmacosystems Biology: from Pharmacogenomics to Pharmaco-Omics. 10.7. Concluding Remarks.
520			_aModern medicine is undergoing a paradigm shift from a "one-size-fits-all" strategy to a more precise patient-customized therapy and medication plan. While the success of precision medicine relies on the level of pharmacogenomic knowledge, dissecting the genetic mechanisms of drug response in a sufficient detail requires powerful computational tools. Quantitative Methods for Precision Medicine: Pharmacogenomics in Action presents the advanced statistical methods for mapping pharmacogenetic control by integrating pharmacokinetic and pharmacodynamic principles of drug-body interactions. Beyond traditional reductionist-based statistical genetic approaches, statistical formulization in this book synthesizes elements of multiple disciplines to infer, visualize, and track how pharmacogenes interact together as an intricate but well-coordinated system to mediate patient-specific drug response. Features: Functional and systems mapping models to characterize the genetic architecture of multiple medication processes Statistical methods for analyzing informative missing data in pharmacogenetic association studies Functional graph theory of inferring genetic interaction networks from association data Leveraging the concept of epistasis to capture its bidirectional, signed and weighted properties Modeling gene-induced cell-cell crosstalk and its impact on drug response A graph model of drug-drug interactions in combination therapies Critical methodological issues to improve pharmacogenomic research as the cornerstone of precision medicine This book is suitable for graduate students and researchers in the field of biology, medicine, bioinformatics and drug design and delivery interested in statistical and computational modelling of biological processes and systems. It may also serve as a major reference for applied mathematicians, computer scientists, and statisticians who attempt to develop algorithmic tools for genetic mapping, systems pharmacogenomics and systems biology. It can be used as both a textbook and research reference. It can also be used by professionals in pharmaceutical sectors who design drugs and by clinical doctors who deliver drugs"--
650		0	_aPrecision medicine _xStatistical methods. _963037
650		0	_aPrecision medicine _xMathematical models. _963038
700	1		_aSang, Mengmeng, _econtributor. _963039
700	1		_aLi, Feng _e(Computational biologist), _963040
942			_2ddc _cBK
999			_c12052 _d12052