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Titlebook: Artificial Intelligence Methods and Tools for Systems Biology; Werner Dubitzky,Francisco Azuaje Book 2004 Springer Science+Business Media

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發(fā)表于 2025-3-21 17:22:43 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
期刊全稱Artificial Intelligence Methods and Tools for Systems Biology
影響因子2023Werner Dubitzky,Francisco Azuaje
視頻videohttp://file.papertrans.cn/163/162128/162128.mp4
學(xué)科分類Computational Biology
圖書封面Titlebook: Artificial Intelligence Methods and Tools for Systems Biology;  Werner Dubitzky,Francisco Azuaje Book 2004 Springer Science+Business Media
影響因子.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 ac
Pindex Book 2004
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QSAR Modeling of Mutagenicity on Non-Congeneric Sets of Organic Compounds,ods from artificial intelligence, quantum mechanics, statistical methods by analyzing relationships between the mutagenic activity of compounds and their structure. The overview is given on the use of artificial intelligence methods for the estimation of mutagenicity. The focus is on ., the selectio
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Characterizing Gene Expression Time Series using a Hidden Markov Model,terizing the developmental processes within the cell. By explicitly modelling the time dependent aspects of these data using a novel form of the HMM, each stage of cell development can be depicted. In this model, the hitherto unknown development process that manifests itself as changes in gene expre
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A Data-Driven, Flexible Machine Learning Strategy for the Classification of Biomedical Data,es, they also present significant challenges for analysis, classification and interpretation. These challenges include sample sparsity, high-dimensional feature spaces, and noise/artifact signatures. Since a dataindependent ‘universal’ classifier does not exist, a classification strategy is needed,
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Cooperative Metaheuristics for Exploring Proteomic Data,pproximated solutions in a reasonable time. Cooperative metaheuristics are a sub-set of metaheuristics, which implies a parallel exploration of the search space by several entities with information exchange between them. Several improvements in the field of metaheuristics are given. A hierarchical a
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