{"product_id":"pharmacogenomics-using-artificial-intelligence-optimizing-drug-response-through-personalized-genomic-analysis-hardback-9781394404438","title":"Pharmacogenomics Using Artificial Intelligence; Optimizing Drug Response through Personalized Genomic Analysis (Hardback) 9781394404438","description":"\u003cfont face=\"Georgia\"\u003e\r\n\u003cp\u003e\u003cfont size=\"6\"\u003ePharmacogenomics Using Artificial Intelligence\u003c\/font\u003e\u003cbr\u003e\r\n\u003cfont size=\"5\"\u003eOptimizing Drug Response through Personalized Genomic Analysis\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\r\n\r\n\u003cp\u003e\u003cfont size=\"4\"\u003eUmesh Kumar Lilhore (Edited by), UK Lilhore (Author), Kaamran Raahemifar (Edited by), Sarita Simaiya (Edited by), R. Sunder (Edited by), R. Lotus (Edited by)\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e9781394404438, Wiley\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eHardback, published 2 July 2026\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e352 pages\u003cbr\u003e22.9 x 15.2 x 2.2 cm, 0.604 kg\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\r\n\r\n\r\n\u003cp align=\"justify\"\u003e\u003cstrong\u003e\u003cfont size=\"3\"\u003e\u003cp\u003e\u003cb\u003eBridging the critical gap between complex genomic data and actual clinical practice, this essential volume delivers the cutting-edge AI methodologies, expert bioinformatics insights, and practical case studies needed to unlock truly personalized medicine.\u003c\/b\u003e \u003c\/p\u003e\n\u003cp\u003eThe intersection of artificial intelligence and pharmacogenomics represents a transformative change in the life sciences industry. Pharmacogenomics, the study of how genetic variations influence an individual’s response to drugs, has long held the promise of enabling personalized treatments that are tailored to the genetic profile of individual patients, improving therapeutic outcomes and minimizing adverse drug reactions. However, the complexity of genomic data, massive scale of information, and challenge of interpreting the intricate relationships between genetic variations and drug responses have impeded the widespread implementation of personalized treatments in clinical practice. This volume explores how AI technologies are transforming personalized medicine by optimizing drug responses based on individual genetic profiles. The book will provide a comprehensive look at the role of AI in advancing pharmacogenomic research and its application in clinical practice, enabling healthcare professionals to predict the most effective and safest drugs for individual patients. \u003c\/p\u003e\n\u003cp\u003eThe book will be structured around the application of cutting-edge AI techniques in analyzing genomic data. Each chapter will highlight different aspects of AI-driven pharmacogenomics, from drug development and genetic variant identification to clinical implementation and ethical considerations. Experts from diverse fields, including bioinformatics, pharmacology, and data science, will contribute insights into how AI can be harnessed to analyze large genomic datasets, predict patient-specific drug responses, and overcome existing challenges in precision medicine. This volume will not only provide theoretical knowledge but also offer practical examples, case studies, and methodologies that researchers, clinicians, and healthcare professionals can utilize to enhance pharmacogenomic research and personalize patient care.\u003c\/p\u003e\u003c\/font\u003e\u003c\/strong\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e\u003cp\u003ePreface xv\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1 Foundations of Pharmacogenomics: Understanding the Genetic Basis of Drug Response 1\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eDhanesh Kumar, Thangiah Sathishkumar, Sarangam Kodati, Venkata Praveen Kumar Vuppala, Rasmi A. and Rajakumar Perumal\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e1.1 Introduction 2\u003cbr\u003e1.2 Genetics of Drug Response Mechanisms 5\u003cbr\u003e1.3 Clinically Actionable Examples 7\u003cbr\u003e1.4 Implementation Frameworks and Clinical Integration 14\u003cbr\u003e1.5 New Technologies and Emerging Trends 19\u003cbr\u003e1.6 Conclusion 20\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 From Data to Therapy: Artificial Intelligence Applications in Pharmacogenomics 23\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eYuvaraj Velusamy, L. Gandhimathi, Shaziya Islam, P. Jyothi, Saranya P. and S. Suresh\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e2.1 Introduction 24\u003cbr\u003e2.2 Data Foundations in AI-Driven Pharmacogenomics 28\u003cbr\u003e2.3 AI Methodologies in PGx 32\u003cbr\u003e2.4 Translating Data to Therapy: Key AI-Driven PGx Applications 36\u003cbr\u003e2.5 Challenges and Limitations 39\u003cbr\u003e2.6 Future Perspectives 42\u003cbr\u003e2.7 Conclusion 45\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 Machine Learning Approaches for Genomic Data Analysis in Pharmacogenomics 49\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eAshwin M., Sreenivas Mekala, V. Arun, Ashish, S. Mathumohan and K. Kaliraj\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e3.1 Introduction 50Contents vii\u003cbr\u003e3.2 Related Works 51\u003cbr\u003e3.3 Methodology 58\u003cbr\u003e3.4 Results and Discussions 65\u003cbr\u003e3.5 Conclusion 71\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 Deep Learning and Neural Networks: Unlocking Complex Patterns in Genomic Medicine 75\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eM. Sudharsan, K. Maithili, T. Ravi, Margaret Mary T., M. Rajesh Khanna and P. Eswaran\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e4.1 Introduction 76\u003cbr\u003e4.2 Related Works 78\u003cbr\u003e4.3 Methodology 81\u003cbr\u003e4.4 Results and Discussions 89\u003cbr\u003e4.5 Conclusion 96\u003cbr\u003e4.6 Future Directions of the Study 96\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 AI-Driven Drug Discovery: Accelerating Therapeutic Innovation through Genomics 101\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eK. Prakash, Phani Kumar Solleti, Tarak Hussain, Chilukala Mahender Reddy, Margaret Mary T. and P. Arumugam\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e5.1 Introduction 102\u003cbr\u003e5.2 Related Works 104\u003cbr\u003e5.3 Methodology 107\u003cbr\u003e5.4 Results and Discussions 113\u003cbr\u003e5.5 Conclusion 119\u003cbr\u003e5.6 Future Directions 120\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 Personalized Medicine Through Pharmacogenomics and AI: A Precision Therapeutics Approach 125\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eDafik, Anto Lourdu Xavier Raj Arockia Selvarathinam, Priya K. V., Sreeram Indraneel, C. Ambhika and Ruth Ramya Kalangi\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e6.1 Introduction 126\u003cbr\u003e6.2 Related Works 128\u003cbr\u003e6.3 Methodology 133\u003cbr\u003e6.4 Results and Discussions 139\u003cbr\u003e6.5 Discussion 143\u003cbr\u003e6.6 Conclusion 145\u003cbr\u003e6.7 Future Directions 146\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 Real-World Use Cases of AI in Pharmacogenomic Decision Support Systems 149\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eKayal Padmanandam, IsaiVani Mariyappan, Anitha D., Sachin Chandravadan Karad, Pooja P. Raj and Umesh Kumar Lihore\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e7.1 Introduction 150\u003cbr\u003e7.2 Background and Rationale 153\u003cbr\u003e7.3 Methodology 155\u003cbr\u003e7.5 Discussion 165\u003cbr\u003e7.6 Challenges and Barriers to Implementation 167\u003cbr\u003e7.7 Future Directions 168\u003cbr\u003e7.8 Conclusion 169\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8 AI Algorithms for Predicting Drug Response in Diverse Populations: Bridging Pharmacogenomics and Precision Medicine 173\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eFathimathul Rajeena P.P., Rahoof P. P. and Sunder R.\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e8.1 Introduction 174x Contents\u003cbr\u003e8.2 Background 177\u003cbr\u003e8.3 Methodology 180\u003cbr\u003e8.4 Results and Findings 184\u003cbr\u003e8.5 Conclusion 193\u003c\/p\u003e \u003cp\u003e\u003cb\u003e9 Artificial Intelligence for Genetic Variant Detection and Interpretation 197\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eLokendra Singh Songare, Narendra B. Mustare, Kamepalli Sujatha, Albin Kurian, Aparajita Mukherjee and Umesh Kumar Lilhore\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e9.1 Introduction 198\u003cbr\u003e9.2 Related Works 200\u003cbr\u003e9.3 Methodology 204\u003cbr\u003e9.4 Results and Findings 208\u003cbr\u003e9.5 Conclusion 215\u003cbr\u003e9.6 Future Directions 216\u003c\/p\u003e \u003cp\u003e\u003cb\u003e10 Cardiovascular Pharmacogenomics: Genetic Predictors of Drug Response and Toxicity 219\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eSunder R., Shanimol Shajan, S. Anupkant, Donamol Joseph, D. Vetrithangam and Rasmi A.\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e10.1 Introduction 220\u003cbr\u003e10.2 Related Works 222\u003cbr\u003e10.3 Methodology 225Contents xi\u003cbr\u003e10.4 Results and Findings 227\u003cbr\u003e10.5 Conclusion 236\u003cbr\u003e10.6 Future Directions 236\u003c\/p\u003e \u003cp\u003e\u003cb\u003e11 Wearable Devices and Real-Time Pharmacogenomic Monitoring 239\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eP. Kavitha, Sruthy Sukumaran, Kavya Clare P. Shaji, S. Chinnapparaj, Veeraiyah Thangasamy and Sunder R.\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e11.1 Introduction 240\u003cbr\u003e11.2 Related Works 242\u003cbr\u003e11.3 Methodology 246\u003cbr\u003e11.4 Results and Findings 248\u003cbr\u003e11.5 General Discussion 253\u003cbr\u003e11.6 Conclusions 254\u003cbr\u003e11.7 Future Directions 255\u003c\/p\u003e \u003cp\u003e\u003cb\u003e12 Challenges and Limitations of Applying Artificial Intelligence in Pharmacogenomic Pipelines: Technical, Clinical, and Operational Perspectives 259\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eYagyesh Godiyal, Maharani Abu Bakar, S. Madhusudhanan, Kochumol Abraham, Aparajita Mukherjee and Sunder R.\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e12.1 Introduction 260\u003cbr\u003e12.2 Thematic Analysis of Challenges 262\u003cbr\u003e12.3 Identification of Repeated Patterns, Bottlenecks 269\u003cbr\u003e12.4 Strategies to Minimize these Challenges 272\u003cbr\u003e12.5 Real-World AI Applications in Pharmacogenomics 277\u003cbr\u003e12.6 Conclusion 280\u003cbr\u003e12.7 Future Research Directions 280\u003c\/p\u003e \u003cp\u003e\u003cb\u003e13 Ethical Frameworks for Integrating AI in Pharmacogenomics: A Focus on Equity and Justice 285\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eIka Hesti Agustin, R. Kannamma, Nallametti Nagarjuna, Sheela S., D. Vetrithangam and Thilagavathi K.\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e13.1 Introduction 286\u003cbr\u003e13.2 Related Works 288\u003cbr\u003e13.3 Research Design 292\u003cbr\u003e13.4 Results and Findings 295\u003cbr\u003e13.5 Conclusion and Future Work 303\u003c\/p\u003e \u003cp\u003e\u003cb\u003e14 The Future of AI in Pharmacogenomics: Trends, Innovations, and Global Perspectives 307\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eSanaj M.S., Minnuja Shelly, Asha S., Nor Asilah Wati Abdul Hamid, S. Mathumohan and Sudhir Ramadass\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e14.1 Introduction 308\u003cbr\u003e14.2 Foundations of AI in Pharmacogenomics 311\u003cbr\u003e14.3 Present Developments in Pharmacogenomics Using AI 313\u003cbr\u003e14.4 Innovations and Emerging Technologies 317\u003cbr\u003e14.5 Global Perspectives and Trends 320\u003cbr\u003e14.6 Challenges and Limitations 322\u003cbr\u003e14.7 Future Directions 323\u003cbr\u003e14.8 Conclusion 324\u003c\/p\u003e \u003cp\u003eReferences 325\u003cbr\u003eIndex 329\u003c\/p\u003e\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eSubject Areas: Chemistry [\u003ca title=\"See our other books on Chemistry\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Chemistry%20%5BPN%5D%22\"\u003ePN\u003c\/a\u003e]\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\u003c\/font\u003e","brand":"Wiley-Scrivener","offers":[{"title":"Brand New","offer_id":52433833492760,"sku":"9781394404438","price":145.99,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0730\/2037\/5320\/files\/9781394404438.jpg?v=1784854814","url":"https:\/\/freshlyprintedbooks.co.uk\/products\/pharmacogenomics-using-artificial-intelligence-optimizing-drug-response-through-personalized-genomic-analysis-hardback-9781394404438","provider":"Freshly Printed Books","version":"1.0","type":"link"}