01055nam 2200337 450 991041206940332120230822004213.0(CKB)5280000000243283(NjHacI)995280000000243283(EXLCZ)99528000000024328320230822d2020 uy 0engur|||||||||||txtrdacontentcrdamediacrrdacarrierProceedings of the 28th ACM Conference on User Modeling, Adaptation and Personalization /Tsvi KuflikNew York, NY :Association for Computing Machinery,2020.©20201 online resource (426 pages)1-4503-6861-1 Computer scienceComputer science.004Kuflik Tsvi933757NjHacINjHaclBOOK9910412069403321Proceedings of the 28th ACM Conference on User Modeling, Adaptation and Personalization3428061UNINA04899nam 22005773 450 991101957380332120240614080242.0978139423419613942341989781394234189139423418X(MiAaPQ)EBC31466376(Au-PeEL)EBL31466376(CKB)32273948700041(OCoLC)1439563870(OCoLC-P)1439563870(CaSebORM)9781394234165(Perlego)4453229(OCoLC)1439599199(EXLCZ)993227394870004120240614d2024 uy 0engurcnu||||||||txtrdacontentcrdamediacrrdacarrierArtificial Intelligence and Machine Learning in Drug Design and Development1st ed.Newark :John Wiley & Sons, Incorporated,2024.©2024.1 online resource (670 pages)Fintech in a Sustainable Digital Society Series9781394234165 1394234163 The book is a comprehensive guide that explores the use of artificial intelligence and machine learning in drug discovery and development covering a range of topics, including the use of molecular modeling, docking, identifying targets, selecting compounds, and optimizing drugs. The intersection of Artificial Intelligence (AI) and Machine Learning (ML) within the field of drug design and development represents a pivotal moment in the history of healthcare and pharmaceuticals. The remarkable synergy between cutting-edge technology and the life sciences has ushered in a new era of possibilities, offering unprecedented opportunities, formidable challenges, and a tantalizing glimpse into the future of medicine. AI can be applied to all the key areas of the pharmaceutical industry, such as drug discovery and development, drug repurposing, and improving productivity within a short period. Contemporary methods have shown promising results in facilitating the discovery of drugs to target different diseases. Moreover, AI helps in predicting the efficacy and safety of molecules and gives researchers a much broader chemical pallet for the selection of the best molecules for drug testing and delivery. In this context, drug repurposing is another important topic where AI can have a substantial impact. With the vast amount of clinical and pharmaceutical data available to date, AI algorithms find suitable drugs that can be repurposed for alternative use in medicine. This book is a comprehensive exploration of this dynamic and rapidly evolving field. In an era where precision and efficiency are paramount in drug discovery, AI and ML have emerged as transformative tools, reshaping the way we identify, design, and develop pharmaceuticals. This book is a testament to the profound impact these technologies have had and will continue to have on the pharmaceutical industry, healthcare, and ultimately, patient well-being. The editors of this volume have assembled a distinguished group of experts, researchers, and thought leaders from both the AI, ML, and pharmaceutical domains. Their collective knowledge and insights illuminate the multifaceted landscape of AI and ML in drug design and development, offering a roadmap for navigating its complexities and harnessing its potential. In each section, readers will find a rich tapestry of knowledge, case studies, and expert opinions, providing a 360-degree view of AI and ML's role in drug design and development. Whether you are a researcher, scientist, industry professional, policymaker, or simply curious about the future of medicine, this book offers 19 state-of-the-art chapters providing valuable insights and a compass to navigate the exciting journey ahead. Audience The book is a valuable resource for a wide range of professionals in the pharmaceutical and allied industries including researchers, scientists, engineers, and laboratory workers in the field of drug discovery and development, who want to learn about the latest techniques in machine learning and AI, as well as information technology professionals who are interested in the application of machine learning and artificial intelligence in drug development.Fintech in a Sustainable Digital Society SeriesDrug developmentDrug development.615.1/9Khanna Abhirup1840581El Barachi May1740982Jain Sapna1840582Kumar Manoj720895Nayyar Anand1379041MiAaPQMiAaPQMiAaPQBOOK9911019573803321Artificial Intelligence and Machine Learning in Drug Design and Development4420159UNINA05190nam 22008175 450 991099967910332120251202150009.09783031843044303184304510.1007/978-3-031-84304-4(CKB)38485083600041(DE-He213)978-3-031-84304-4(MiAaPQ)EBC32011951(Au-PeEL)EBL32011951(EXLCZ)993848508360004120250417d2025 u| 0engur|||||||||||txtrdacontentcrdamediacrrdacarrierAdvanced Portfolio Optimization A Cutting-edge Quantitative Approach /by Dany Cajas1st ed. 2025.Cham :Springer Nature Switzerland :Imprint: Springer,2025.1 online resource (XV, 503 p. 216 illus., 186 illus. in color.)9783031843037 3031843037 Chapter 1 Introduction -- Chapter 2 Why use Python? -- Part I Parameter Estimation -- Chapter 3 Sample Based Methods -- Chapter 4 Risk Factors Models -- Chapter 5 Black Litterman Models -- Chapter 7 Convex Risk Measures -- Chapter 8 Return-Risk Trade-Off Optimization -- Chapter 9 Real Features Constraints -- Chapter 10 Risk Parity Optimization -- Chapter 11 Robust Optimization -- Part III Machine Learning Portfolio Optimization -- Chapter 12 Hierarchical Clustering Portfolios -- Chapter 13 Graph Theory Based Portfolios -- Part IV Backtesting -- Chapter 14 Generation of Synthetic Data -- Chapter 15 Backtesting Process -- Part V Appendix -- Chapter A Linear Algebra -- Chapter B Convex Optimization -- Chapter C Mixed Integer Programming.This book is an innovative and comprehensive guide that provides readers with the knowledge about the latest trends, models and algorithms used to build investment portfolios and the practical skills necessary to apply them in their own investment strategies. It integrates latest advanced quantitative techniques into portfolio optimization, raises questions about which alternatives to modern portfolio theory exists and how they can be applied to improve the performance of multi-asset portfolios. It provides answers and solutions by offering practical tools and code samples that enable readers to implement advanced portfolio optimization techniques and make informed investment decisions. Portfolio Optimization goes beyond traditional portfolio theory (Quadratic Programming), incorporating last advances in convex optimization techniques and cutting-edge machine learning algorithms. It extensively addresses risk management and uncertainty quantification, teaching readers how to measure and minimize various forms of risk in their portfolios. This book goes beyond traditional back testing methodologies based on historical data for investment portfolios, incorporating tools to create synthetic datasets and robust methodologies to identify better investment strategies considering real aspects like transaction costs. The author provides several methodologies for estimating the input parameters of investment portfolio optimization models, from classical statistics to more advanced models, such as graph-based estimators and Bayesian estimators, provide a deep understanding of advanced convex optimization models and machine learning algorithms for building investment portfolios and the necessary tools to design the back testing of investment portfolios using several methodologies based on historical and synthetic datasets that allow readers identify the better investment strategies.StatisticsData miningMachine learningValuationFinancial risk managementStatistics in Business, Management, Economics, Finance, InsuranceData Mining and Knowledge DiscoveryMachine LearningInvestment AppraisalRisk ManagementEstadísticathubMineria de dadesthubAprenentatge automàticthubValoracióthubGestió del riscthubEstadística econòmicathubLlibres electrònicsthubStatistics.Data mining.Machine learning.Valuation.Financial risk management.Statistics in Business, Management, Economics, Finance, Insurance.Data Mining and Knowledge Discovery.Machine Learning.Investment Appraisal.Risk Management.EstadísticaMineria de dadesAprenentatge automàticValoracióGestió del riscEstadística econòmica300.727Cajas Danyauthttp://id.loc.gov/vocabulary/relators/aut1817231MiAaPQMiAaPQMiAaPQBOOK9910999679103321Advanced Portfolio Optimization4374791UNINA