Artificial Intelligence for Molecular Biology: Fundamental Methods and Applications
暫譯: 分子生物學的人工智慧：基本方法與應用

Nabeel Asim, Muhammad, Ahmed, Sheraz, Dengel, Andreas

Artificial Intelligence for Molecular Biology: Fundamental Methods and Applications
  • 出版商: Springer
  • 出版日期: 2025-10-29
  • 售價: $3,490
  • 貴賓價: 9.5 折 $3,316
  • 語言: 英文
  • 頁數: 544
  • 裝訂: Hardcover - also called cloth, retail trade, or trade
  • ISBN: 3031904494
  • ISBN-13: 9783031904493
  • 相關分類: AI Coding

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商品描述

Molecular biology is at the forefront of scientific discovery, unraveling the intricacies of life at the most fundamental level. As biological systems become increasingly complex and data-rich, artificial intelligence (AI) has emerged as a pivotal tool for unlocking new insights and enhancing our understanding of these systems. This first volume focuses on the core principles of molecular biology while introducing AI-driven approaches to genomic and proteomic sequence analysis. It serves as a foundation for integrating computational methodologies into the study of biological systems.

The chapters in this volume are structured to provide a comprehensive overview of the essential concepts, tools, and methodologies in molecular biology, enriched by the latest advancements in AI:

    Fundamentals of Molecular Biology: This chapter delves into the foundational elements of molecular biology, exploring the central dogma, gene expression regulation, cellular organization, and the evolution of genome studies. It also highlights the role of computational biology in complementing traditional experimental techniques. DNA, RNA, & Protein Structures: Understanding the structural intricacies of DNA, RNA, and proteins is crucial for comprehending their functions. This chapter outlines their fundamental properties and sets the stage for discussing AI-driven sequence analysis. Exploration of AI-Driven Genomic and Proteomic Sequence Analysis Landscape: This section provides an in-depth look at how AI is reshaping the field of sequence analysis. Topics include representation learning, feature engineering, predictive modeling, and an evaluation of performance metrics for AI-driven pipelines. Insights of Biological Databases: Biological data is the backbone of molecular biology research. This chapter discusses the structure, organization, and utilization of key databases, emphasizing data formats, redundancy issues, and retrieval systems. DNA & RNA Sequence Representation Learning Methods: Representing nucleotide sequences in ways that AI models can process effectively is a critical challenge. This chapter explores various encoding methods, from nucleotide distributions to Fourier transformations, providing a robust toolkit for researchers. Protein Sequence Representation Learning Methods: Similar to nucleic acid sequences, encoding protein sequences requires sophisticated techniques. This section details diverse methodologies, including physicochemical properties, z-scales, and context-aware encodings. CRISPR System and AI Applications: CRISPR technology has revolutionized genetic editing, and AI is accelerating its potential. This chapter examines AI-driven approaches to CRISPR-related tasks, from predictive modeling to dataset development, emphasizing the synergy between these transformative technologies.
Through this volume, readers will gain a solid understanding of molecular biology and its convergence with AI. The interdisciplinary approach ensures that the biological complexities are complemented by computational rigor, laying the groundwork for the second volume, which delves deeper into advanced AI applications in molecular biology.

商品描述(中文翻譯)

分子生物學位於科學發現的最前沿，揭示生命在最基本層面的複雜性。隨著生物系統變得越來越複雜且數據豐富，人工智慧（AI）已成為解鎖新見解和增強我們對這些系統理解的關鍵工具。本卷專注於分子生物學的核心原則，同時介紹基於AI的基因組和蛋白質組序列分析方法。它為將計算方法整合到生物系統的研究中奠定了基礎。

本卷的章節結構旨在提供分子生物學中基本概念、工具和方法論的全面概述，並融入最新的AI進展：

1. **分子生物學基礎**：本章深入探討分子生物學的基礎要素，探索中心法則、基因表達調控、細胞組織以及基因組研究的演變。它還強調計算生物學在補充傳統實驗技術中的作用。

2. **DNA、RNA與蛋白質結構**：理解DNA、RNA和蛋白質的結構複雜性對於理解其功能至關重要。本章概述了它們的基本特性，並為討論基於AI的序列分析奠定基礎。

3. **AI驅動的基因組和蛋白質組序列分析的探索**：本節深入探討AI如何重塑序列分析領域。主題包括表示學習、特徵工程、預測建模，以及對AI驅動管道性能指標的評估。

4. **生物數據庫的見解**：生物數據是分子生物學研究的基石。本章討論了關鍵數據庫的結構、組織和利用，強調數據格式、冗餘問題和檢索系統。

5. **DNA與RNA序列表示學習方法**：以AI模型能有效處理的方式表示核苷酸序列是一項關鍵挑戰。本章探討了各種編碼方法，從核苷酸分佈到傅立葉變換，為研究人員提供了一套強大的工具包。

6. **蛋白質序列表示學習方法**：與核酸序列類似，編碼蛋白質序列需要複雜的技術。本節詳細介紹了多種方法，包括物理化學特性、z-尺度和上下文感知編碼。

7. **CRISPR系統與AI應用**：CRISPR技術徹底改變了基因編輯，而AI正在加速其潛力。本章檢視了AI驅動的CRISPR相關任務的方法，從預測建模到數據集開發，強調這些變革性技術之間的協同作用。

通過本卷，讀者將對分子生物學及其與AI的交匯有一個扎實的理解。這種跨學科的方法確保生物複雜性與計算嚴謹性相輔相成，為第二卷的深入探討奠定基礎，該卷將更深入地探討分子生物學中的高級AI應用。

作者簡介

Muhammad Nabeel Asim is a Senior Researcher at the German Research Center for Artificial Intelligence (DFKI) and a co-founder of intelligentX. He earned his Ph.D. with summa cum laude distinction from Technische Universität Kaiserslautern, Germany, where his research focused on developing an AI-driven framework capable of generating innovative predictive pipelines for genomics, proteomics, and multi-omics data analysis. Nabeel has an extensive publication record in areas such as DNA, RNA, and protein sequence analysis. Beyond genomics, he has applied his expertise in artificial intelligence to create diverse real-world solutions across various domains, including natural language processing, energy, and network security. Currently, he is committed to mentoring future researchers and developing innovative AI solutions to address critical global challenges.

Sheraz Ahmed is a Principal Researcher at the German Research Center for Artificial Intelligence (DFKI) while running DeepReader GmbH, a company he founded to bridge the gap between academic research and industry applications. He earned his Ph.D. from Technische Universität Kaiserslautern, Germany, focusing on innovative approaches to breaking down and understanding information in document images. His work has increasingly turned toward the Life Sciences, where he sees AI as a powerful tool for accelerating scientific breakthroughs. He is also dedicated to advancing trustworthy AI, ensuring that AI technologies are ethical, transparent, and reliable for widespread adoption. In recognition of his outstanding ocontributions, Sheraz was honored with the prestigious DFKI Research Fellow Award, highlighting his leadership in the field of artificial intelligence. Multiple research stays backed by prestigious JSPS and DAAD fellowships, have shaped his international outlook on AI development. Today, he continues to explore new frontiers in AI while mentoring the next generation of researchers and building practical solutions for real-world challenges.

Andreas Dengel is a professor at the Department of Computer Science at the University of Kaiserslautern-Landau, a co-founder of intelligentX as well as the Executive Director of DFKI in Kaiserslautern. Since 2009, he has held another professorship (kyakuin) at the Department of Computer Science and Intelligent Systems at Osaka Metropolitan University, with the right to teach and examine. At this university, he was also appointed "Distinguished Honorary Professor" (tokubetu eiyo kyoju) in March 2018, an honor bestowed on only five researchers in 135 years. He has received many honors for his work and scientific achievements. In 2019 he was selected by a jury on behalf of the German Federal Ministry of Education and Research (BMBF) as one of the most influential scientists in 50 years of AI history in Germany for his research in the field of document analysis. He is the recipient of the Order of Merit of Rhineland-Palatinate and was awarded the "The Order of the Rising Sun, Gold Rays with Neck Ribbon" in 2021, Japan's oldest order, on behalf of His Majesty Emperor Naruhito. Andreas Dengel has chaired numerous international conferences and is a member of the editorial boards of international journals and book series. He has written or edited 14 books and is the author of more than 600 peer-reviewed scientific publications, many of which have received the Best Paper Award. His main research areas are machine learning, pattern recognition, quantified learning, data mining, and neuro-symbolic AI.

作者簡介(中文翻譯)

穆罕默德·納比爾·阿西姆（Muhammad Nabeel Asim）是德國人工智慧研究中心（DFKI）的高級研究員，也是intelligentX的共同創辦人。他在德國凱瑟斯勞滕工業大學（Technische Universität Kaiserslautern）獲得了以優異成績（summa cum laude）取得的博士學位，研究重點是開發一個能夠生成創新預測管道的人工智慧驅動框架，應用於基因組學、蛋白質組學和多組學數據分析。納比爾在DNA、RNA和蛋白質序列分析等領域擁有廣泛的出版紀錄。除了基因組學，他還將自己的人工智慧專業知識應用於創造多樣的現實世界解決方案，涵蓋自然語言處理、能源和網絡安全等多個領域。目前，他致力於指導未來的研究者，並開發創新的人工智慧解決方案，以應對全球面臨的重大挑戰。

謝拉茲·艾哈邁德（Sheraz Ahmed）是德國人工智慧研究中心（DFKI）的首席研究員，同時經營他創辦的DeepReader GmbH，該公司旨在縮短學術研究與產業應用之間的距離。他在德國凱瑟斯勞滕工業大學獲得博士學位，專注於創新方法以解析和理解文件圖像中的信息。他的研究逐漸轉向生命科學，認為人工智慧是加速科學突破的強大工具。他還致力於推進可信的人工智慧，確保人工智慧技術在廣泛應用中是道德的、透明的和可靠的。因其卓越的貢獻，謝拉茲獲得了享有盛譽的DFKI研究員獎，突顯了他在人工智慧領域的領導地位。多次獲得JSPS和DAAD獎學金支持的研究經歷，塑造了他對人工智慧發展的國際視野。如今，他繼續探索人工智慧的新前沿，同時指導下一代研究者，並為現實世界的挑戰構建實用解決方案。

安德烈亞斯·登格爾（Andreas Dengel）是凱瑟斯勞滕-蘭道大學計算機科學系的教授，也是intelligentX的共同創辦人，以及DFKI凱瑟斯勞滕的執行董事。自2009年以來，他在大阪市立大學的計算機科學與智能系擔任另一個教授職位（客員教授），擁有教學和考試的權利。在這所大學，他於2018年3月被任命為「傑出名譽教授」（特別榮譽教授），這一榮譽在135年來僅授予五位研究者。他因其工作和科學成就獲得了許多榮譽。2019年，他被德國聯邦教育與研究部（BMBF）選為50年人工智慧歷史中最具影響力的科學家之一，因其在文件分析領域的研究而受到認可。他是萊茵-普法爾茨州功績勳章的獲得者，並於2021年獲得日本最古老的勳章「旭日章金光帶」，由德仁天皇代表頒發。安德烈亞斯·登格爾主持過多個國際會議，並擔任國際期刊和書籍系列的編輯委員會成員。他撰寫或編輯了14本書籍，並發表了超過600篇經過同行評審的科學出版物，其中許多獲得了最佳論文獎。他的主要研究領域包括機器學習、模式識別、量化學習、數據挖掘和神經符號人工智慧。

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