LEADER 05154nam 22006375 450 001 9910437861803321 005 20200701063241.0 010 $a1-4614-6849-3 024 7 $a10.1007/978-1-4614-6849-3 035 $a(CKB)3390000000037144 035 $a(SSID)ssj0000904250 035 $a(PQKBManifestationID)11943813 035 $a(PQKBTitleCode)TC0000904250 035 $a(PQKBWorkID)10908879 035 $a(PQKB)10545081 035 $a(DE-He213)978-1-4614-6849-3 035 $a(MiAaPQ)EBC6245972 035 $a(MiAaPQ)EBC1317001 035 $a(Au-PeEL)EBL1317001 035 $a(CaPaEBR)ebr10969096 035 $a(OCoLC)870244221 035 $a(PPN)170487997 035 $a(EXLCZ)993390000000037144 100 $a20130517d2013 u| 0 101 0 $aeng 135 $aurnn#008mamaa 181 $ctxt 182 $cc 183 $acr 200 10$aApplied Predictive Modeling /$fby Max Kuhn, Kjell Johnson 205 $a1st ed. 2013. 210 1$aNew York, NY :$cSpringer New York :$cImprint: Springer,$d2013. 215 $a1 online resource (XIII, 600 p. 203 illus., 153 illus. in color.) 300 $aBibliographic Level Mode of Issuance: Monograph 311 $a1-4614-6848-5 320 $aIncludes bibliographical references (pages 569-587) and index. 327 $aGeneral Strategies -- Regression Models -- Classification Models -- Other Considerations -- Appendix -- References -- Indices. 330 $aThis text is intended for a broad audience as both an introduction to predictive models as well as a guide to applying them. Non-mathematical readers will appreciate the intuitive explanations of the techniques while an emphasis on problem-solving with real data across a wide variety of applications will aid practitioners who wish to extend their expertise. Readers should have knowledge of basic statistical ideas, such as correlation and linear regression analysis. While the text is biased against complex equations, a mathematical background is needed for advanced topics. Dr. Kuhn is a Director of Non-Clinical Statistics at Pfizer Global R&D in Groton Connecticut. He has been applying predictive models in the pharmaceutical and diagnostic industries for over 15 years and is the author of a number of R packages. Dr. Johnson has more than a decade of statistical consulting and predictive modeling experience in pharmaceutical research and development. He is a co-founder of Arbor Analytics, a firm specializing in predictive modeling and is a former Director of Statistics at Pfizer Global R&D. His scholarly work centers on the application and development of statistical methodology and learning algorithms. Applied Predictive Modeling covers the overall predictive modeling process, beginning with the crucial steps of data preprocessing, data splitting and foundations of model tuning. The text then provides intuitive explanations of numerous common and modern regression and classification techniques, always with an emphasis on illustrating and solving real data problems. Addressing practical concerns extends beyond model fitting to topics such as handling class imbalance, selecting predictors, and pinpointing causes of poor model performance?all of which are problems that occur frequently in practice. The text illustrates all parts of the modeling process through many hands-on, real-life examples. And every chapter contains extensive R code for each step of the process. The data sets and corresponding code are available in the book?s companion AppliedPredictiveModeling R package, which is freely available on the CRAN archive. This multi-purpose text can be used as an introduction to predictive models and the overall modeling process, a practitioner?s reference handbook, or as a text for advanced undergraduate or graduate level predictive modeling courses. To that end, each chapter contains problem sets to help solidify the covered concepts and uses data available in the book?s R package. Readers and students interested in implementing the methods should have some basic knowledge of R. And a handful of the more advanced topics require some mathematical knowledge. . 606 $aStatistics 606 $aStatistics for Life Sciences, Medicine, Health Sciences$3https://scigraph.springernature.com/ontologies/product-market-codes/S17030 606 $aStatistics and Computing/Statistics Programs$3https://scigraph.springernature.com/ontologies/product-market-codes/S12008 606 $aStatistics, general$3https://scigraph.springernature.com/ontologies/product-market-codes/S0000X 615 0$aStatistics. 615 14$aStatistics for Life Sciences, Medicine, Health Sciences. 615 24$aStatistics and Computing/Statistics Programs. 615 24$aStatistics, general. 676 $a519.5 700 $aKuhn$b Max$4aut$4http://id.loc.gov/vocabulary/relators/aut$0524999 702 $aJohnson$b Kjell$4aut$4http://id.loc.gov/vocabulary/relators/aut 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910437861803321 996 $aApplied Predictive Modeling$92528198 997 $aUNINA LEADER 06583nam 2201717z- 450 001 9910557505903321 005 20210501 035 $a(CKB)5400000000044497 035 $a(oapen)https://directory.doabooks.org/handle/20.500.12854/68521 035 $a(oapen)doab68521 035 $a(EXLCZ)995400000000044497 100 $a20202105d2021 |y 0 101 0 $aeng 135 $aurmn|---annan 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 00$aFlavonoids and Their Disease Prevention and Treatment Potential 210 $aBasel, Switzerland$cMDPI - Multidisciplinary Digital Publishing Institute$d2021 215 $a1 online resource (346 p.) 311 08$a3-0365-0000-6 311 08$a3-0365-0001-4 330 $aFlavonoids are ubiquitously present in plant-based foods and natural health products. The molecule of flavonoids is characterized by a 15-carbon skeleton of C6-C3-C6, with the different structural configuration of subclasses. The major subclasses of flavonoids with health-promotional properties are the flavanols or catechins (e.g., epigallocatechin 3-gallate from green tea), the flavones (e.g., apigenin from celery), the flavonols (e.g., quercetin glycosides from apples, berries, and onion), the flavanones (e.g., naringenin from citrus), the anthocyanins (e.g., cyanidin-3-O-glucoside from berries), and the isoflavones (e.g., genistein from soya beans). Scientific evidence has strongly shown that regular intake of dietary flavonoids in efficacious amounts reduces the risk of oxidative stress- and chronic inflammation-mediated pathogenesis of human diseases such as cardiovascular disease, certain cancers, and neurological disorders. The physiological benefits of dietary flavonoids have been demonstrated to be due to multiple mechanisms of action, including regulating redox homeostasis, epigenetic regulations, activation of survival genes and signaling pathways, regulation of mitochondrial function and bioenergetics, and modulation of inflammation response. The role of flavonoids on gut microbiota and the impact of microbial metabolites of flavonoids on optimal health has begun to unravel. The complex physiological modulations of flavonoid molecules are due to their structural diversity. However, some flavonoids are not absorbed well, and their bioavailability could be enhanced through structural modifications and applications of nanotechnology, such as encapsulation. 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