Artificial intelligence methods and tools for systems biology [electronic resource]
- 其他作者：
- 出版： Dordrecht ;Norwell, MA : Springer
- 叢書名： Computational Biology,v. 5
- 主題： Artificial intelligence--Biological applications , Biology--Classification--Data processing. , Systems biology--Research. , Biological systems--Research--Methodology. , Artificial intelligence , Computational Biology , Intelligence artificielle--Applications biologiques. , Biologie--Classification--Informatique. , Systemes biologiques--Recherche--Methodologie.
- ISBN： 9781402058110 (electronic bk.) 、 9781402028595 (paper)
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FIND@SFXID:
CGU
- 資料類型: 電子書
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讀者標籤：
- 系統號： 005042316 | 機讀編目格式
館藏資訊
This book provides simultaneously a design blueprint, user guide, research agenda, and communication platform for current and future developments in artificial intelligence (AI) approaches to systems biology. It places an emphasis on the molecular dimension of life phenomena and in one chapter on anatomical and functional modeling of the brain. As design blueprint, the book is intended for scientists and other professionals tasked with developing and using AI technologies in the context of life sciences research. As a user guide, this volume addresses the requirements of researchers to gain a basic understanding of key AI methodologies for life sciences research. Its emphasis is not on an intricate mathematical treatment of the presented AI methodologies. Instead, it aims at providing the users with a clear understanding and practical know-how of the methods. As a research agenda, the book is intended for computer and life science students, teachers, researchers, and managers who want to understand the state of the art of the presented methodologies and the areas in which gaps in our knowledge demand further research and development. Our aim was to maintain the readability and accessibility of a textbook throughout the chapters, rather than compiling a mere reference manual. The book is also intended as a communication platform seeking to bride the cultural and technological gap among key systems biology disciplines. To support this function, contributors have adopted a terminology and approach that appeal to audiences from different backgrounds.