Formal Methods for the Analysis of Biomedical Ontologies
暫譯: 生物醫學本體分析的形式方法

Zhang, Guo-Qiang, Abeysinghe, Rashmie, Cui, Licong

Formal Methods for the Analysis of Biomedical Ontologies

商品描述

The book synthesizes research on the analysis of biomedical ontologies using formal concept analysis, including through auditing, curation, and enhancement. As the evolution of biomedical ontologies almost inevitably involves manual work, formal methods are a particularly useful tool for ontological engineering and practice, particularly in uncovering unexpected "bugs" and content materials.

The book first introduces simple but formalized strategies for discovering undesired and incoherent patterns in ontologies before exploring the application of formal concept analysis for semantic completeness. The book then turns to formal concept analysis, a classical approach used in the mathematical treatment of orders and lattices, as an ontological engineering principle, focusing on the structural property of ontologies with respect to its conformation to lattice or not (non-lattice). The book helpfully covers the development of more efficient algorithms for non-lattice detection and extraction required by exhaustive lattice/non-lattice analysis. The book goes on to highlight the power and utility of uncovering non-lattice structure for debugging ontologies and describes methods that leverage the linguistic information in concept names (labels) for ontological analysis. It also addresses visualization and performance evaluation issues before closing with an overview and forward-looking perspectives on the field.

This book is intended for graduate students and researchers interested in biomedical ontologies and their applications. It can be a useful supplement for courses on knowledge representation and engineering and also provide readers with a reference for related scientific publications and literature to assist in identifying potential research topics. All mathematical concepts and notations used in this book can be found in standard discrete mathematics textbooks, and the appendix at the end of the book provides a list of key ontological resources, as well as annotated non-lattice and lattice examples that were discovered using the authors' methods, demonstrating how "bugs are fixed" by converting non-lattices to lattices with minimal edit changes.

商品描述(中文翻譯)

本書綜合了使用形式概念分析對生物醫學本體進行分析的研究，包括審核、策展和增強。由於生物醫學本體的演變幾乎不可避免地涉及手動工作，形式方法成為本體工程和實踐中特別有用的工具，尤其是在揭示意外的「錯誤」和內容材料方面。

本書首先介紹了發現本體中不希望出現的和不一致模式的簡單但正式化的策略，然後探討形式概念分析在語義完整性方面的應用。接著，本書轉向形式概念分析，這是一種在數學上處理序和格的經典方法，作為本體工程的原則，重點關注本體的結構特性，特別是其是否符合格的結構（非格）。本書有助於涵蓋為徹底的格/非格分析所需的非格檢測和提取的更高效算法的發展。本書進一步強調揭示非格結構在調試本體中的力量和實用性，並描述利用概念名稱（標籤）中的語言信息進行本體分析的方法。它還討論了可視化和性能評估問題，最後以對該領域的概述和前瞻性展望作結。

本書旨在為對生物醫學本體及其應用感興趣的研究生和研究人員提供參考。它可以作為知識表示和工程課程的有用補充，並為讀者提供相關科學出版物和文獻的參考，以協助識別潛在的研究主題。本書中使用的所有數學概念和符號均可在標準的離散數學教科書中找到，書末的附錄提供了關鍵本體資源的列表，以及使用作者的方法發現的帶註釋的非格和格的示例，展示了如何通過最小的編輯變更將非格轉換為格來「修復錯誤」。

作者簡介

Guo-Qiang "GQ" Zhang is Professor of Medicine, Biomedical Informatics, and Public Health in the University of Texas Health Science Center at Houston (UTHealth Houston). He serves as Co-director for the Texas Institute for Restorative Neurotechnologies and UTHealth's Vice President and Chief Data Scientist. Before joining UTHealth Houston, he served as the inaugural Director of the Institute for Biomedical Informatics, Chief of the Division of Biomedical Informatics, and Associate Director of the Center for Clinical and Translational Science at the University of Kentucky. He spent prior years as professor in the Case School of Engineering and in the School of Medicine at Case Western Reserve University, where he created the Division of Biomedical Informatics in the School of Medicine. GQ Zhang received his Ph.D. in Computer Science from Cambridge University. His research spans clinical and research informatics, data science, neuroinformatics, and biomedical ontologies. During the last decade, he led a research group that has developed production-strength, informatics tools for data capturing, data management, cohort discovery, and clinical decision support, resulting in over 200 scientific publications and multiple awards across the National Institutes of Health (NIH) institutes and the National Science Foundation (NSF).
Licong Cui received her Ph.D. in Computer Science from Case Western Reserve University. She is an assistant professor in School of Biomedical Informatics at the University of Texas Health Science Center at Houston. Before joining UTHealth, she was an assistant professor in the Department of Computer Science and member of the Institute for Biomedical Informatics at the University of Kentucky. Her research interests include ontologies and terminologies, neuroinformatics, big data analytics, large-scale data integration and management, and information extraction and retrieval. She has been a Principal Investigator of several highly competitive research awards funded by the NIH and the NSF. She is a recipient of the prestigious NSF CAREER Award.
Rashmie Abeysinghe received his B.S. in Computer Science from University of Peradeniya, Peradeniya, Sri Lanka and Ph.D. in Computer Science from University of Kentucky. He completed a Summer Internship at the National Library of Medicine, NIH. After completing his Ph.D. study, he joined the Department of Neurology, McGovern Medical School at the University of Texas Health Science Center at Houston as a Research Scientist. His research interests revolve around biomedical ontologies particularly from a quality assurance perspective, information extraction, and deep learning. His paper won a Distinguished Paper Award at the 2021 American Medical Informatics Association (AMIA) Annual Symposium. His papers were also selected as finalists for both the 2018 and 2019 AMIA Annual Symposium Student Paper Competitions.

作者簡介(中文翻譯)

郭強（GQ）張是德克薩斯州休士頓健康科學中心（UTHealth Houston）醫學、生物醫學資訊學及公共衛生的教授。他擔任德克薩斯修復神經技術研究所的共同主任及UTHealth的副總裁和首席數據科學家。在加入UTHealth Houston之前，他曾擔任肯塔基大學生物醫學資訊研究所的首任主任、生物醫學資訊部門的主任，以及臨床與轉化科學中心的副主任。他在凱斯西儲大學的工程學院和醫學院擔任教授，並在醫學院創建了生物醫學資訊部門。GQ 張在劍橋大學獲得計算機科學博士學位。他的研究涵蓋臨床和研究資訊學、數據科學、神經資訊學及生物醫學本體論。在過去十年中，他領導的研究小組開發了生產級的資訊工具，用於數據捕獲、數據管理、隊列發現和臨床決策支持，並因此發表了超過200篇科學論文，獲得了來自國家衛生研究院（NIH）和國家科學基金會（NSF）的多項獎項。

崔麗聰（Licong Cui）在凱斯西儲大學獲得計算機科學博士學位。她是德克薩斯州休士頓健康科學中心生物醫學資訊學院的助理教授。在加入UTHealth之前，她曾是肯塔基大學計算機科學系的助理教授及生物醫學資訊研究所的成員。她的研究興趣包括本體論和術語、神經資訊學、大數據分析、大規模數據整合與管理，以及信息提取和檢索。她曾是多項由NIH和NSF資助的高度競爭性研究獎項的首席研究員，並獲得了享有盛譽的NSF CAREER獎。

拉什米·阿貝辛赫（Rashmie Abeysinghe）在斯里蘭卡佩拉德尼亞大學獲得計算機科學學士學位，並在肯塔基大學獲得計算機科學博士學位。他在國家醫學圖書館（NIH）完成了暑期實習。完成博士學位後，他加入德克薩斯州休士頓健康科學中心麥戈文醫學院的神經學系擔任研究科學家。他的研究興趣圍繞生物醫學本體論，特別是從質量保證的角度、信息提取和深度學習。他的論文在2021年美國醫學資訊協會（AMIA）年會上獲得了傑出論文獎。他的論文也在2018年和2019年AMIA年會學生論文競賽中被選為決賽入圍作品。

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