Quantitative Methods for Precision Medicine: Pharmacogenomics in Action

Modern 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.

現代醫學正在從一刀切的策略轉變為更精確的個體化治療和藥物計劃。儘管精準醫學的成功取決於藥物基因組學知識的水平，但要深入研究藥物反應的遺傳機制，需要強大的計算工具。《精準醫學的定量方法：藥物基因組學實踐》介紹了將藥物動力學和藥效學原理與藥物-身體相互作用的統計方法相結合，以繪製藥物基因控制的高級統計方法。本書的統計公式化綜合了多個學科的要素，以推斷、可視化和追踪藥物基因如何作為一個複雜但協調良好的系統相互作用，調節個體化藥物反應。

特點：
- 功能和系統映射模型，用於表徵多種藥物過程的遺傳結構
- 分析藥物基因關聯研究中有信息缺失數據的統計方法
- 從關聯數據推斷遺傳相互作用網絡的功能圖論
- 利用上位性的概念捕捉其雙向、有符號和加權特性
- 建模基因誘導的細胞間相互作用及其對藥物反應的影響
- 藥物組合療法中藥物相互作用的圖模型
- 改進藥物基因組學研究的關鍵方法論問題，作為精準醫學的基石

本書適合生物學、醫學、生物信息學和藥物設計和傳遞領域的研究生和研究人員，對生物過程和系統的統計和計算建模感興趣。它也可以作為應用數學家、計算機科學家和統計學家的主要參考資料，他們試圖開發用於遺傳映射、系統藥物基因組學和系統生物學的算法工具。它既可以作為教科書，也可以作為研究參考資料。同時，藥物設計領域的專業人士和臨床醫生也可以使用本書。

Rongling Wu develops a transdisciplinary approach for building up multilayer, multiscale and multifunctional bridges that link genotype to phenotype. Dr. Wu asks, answers, and disseminates biologically meaningful questions in the boundaries of statistics, genetics, ecology, and evolution. He invented a statistical method called functional mapping to reveal the genetic architecture of developmental trajectories and incorporated this approach into the context of eco-evo-devo research aimed to unveil the genetic and ecological mechanisms underpinning evolutionary novelties. More recently, Dr. Wu has integrated evolutionary game theory and predator-prey theory through naturally omnipresent allometric scaling law into graph theory to create a new theory - functional game-graph theory. This new theory can unravel the internal workings of complex systems at an unprecedented level of detail by charting and tracing the causal, signed, and weighted roadmap of relationships among high- or even ultrahigh-dimensional variables from any data domains. The second part of this book represents the application of this theory to pharmacogenomic research. Dr. Wu received his PhD in forest genetics at the University of Washington in 1995. He was appointed as Assistant Professor of Statistics at the University of Florida in 2000 and awarded the University Foundation Professorship in 2007. Since 2008, Dr. Wu has been Professor of Public Health Sciences and Statistics, promoted to Distinguished Professor in 2015, and served as Director of the Center of Statistical Genetics at The Pennsylvania State University. Dr. Wu is a Fellow of the American Statistical Association and a Fellow of American Association for the Advancement of Science.

Dr. Sang and Ms. Feng are collaborators of this book writing project under the leadership of Dr. Wu. Dr. Sang got his PhD degree at Beijing Forestry University in 2019 and is currently working as Lecturer of Medical Informatics at Nantong University Medical School, China. Ms. Feng is a PhD candidate in computational biology at Beijing Forestry University and going to graduate in 2023. The research interest of both Dr. Sang and Ms. Feng lies in the statistical modeling of computational biology and its applications to a broader area of agriculture, forestry, medicine, and beyond.

吳榮鈴博士開發了一種跨學科的方法，用於建立多層次、多尺度和多功能的橋樑，將基因型與表型相連。吳博士在統計學、遺傳學、生態學和進化學的邊界上提出、回答和傳播具有生物學意義的問題。他發明了一種稱為功能映射的統計方法，用於揭示發育軌跡的遺傳結構，並將此方法納入生態-進化-發育研究的背景中，旨在揭示支撐進化新特徵的遺傳和生態機制。最近，吳博士通過自然普遍存在的全尺度定律，將進化博弈理論和捕食-被捕食理論與圖論相結合，創造了一個新的理論-功能博弈圖論。這個新理論可以通過繪製和追蹤高維或甚至超高維變量之間的因果、有符號和加權關係的路線圖，在前所未有的細節水平上解開複雜系統的內部運作。本書的第二部分介紹了這一理論在藥物基因組學研究中的應用。吳博士於1995年在華盛頓大學獲得森林遺傳學博士學位。他於2000年被任命為佛羅里達大學統計學助理教授，並於2007年獲得大學基金會教授職位。自2008年以來，吳博士一直擔任賓夕法尼亞州立大學公共衛生科學和統計學教授，並於2015年晉升為傑出教授，並擔任統計遺傳學中心主任。吳博士是美國統計學會和美國科學促進協會的會士。

桑博士和馮女士是吳博士領導下的本書撰寫項目的合作者。桑博士於2019年在北京林業大學獲得博士學位，目前在中國南通大學醫學院擔任醫學信息學講師。馮女士是北京林業大學計算生物學的博士候選人，預計於2023年畢業。桑博士和馮女士的研究興趣在於統計建模的計算生物學及其在農業、林業、醫學等更廣泛領域的應用。