Artificial Intelligence in Bioinformatics (eBook)

Artificial Intelligence in Bioinformatics: From Omics Analysis to Deep Learning and Network Mining reviews the main applications of the topic, from omics analysis to deep learning and network mining. The book includes a rigorous introduction on bioinformatics, also reviewing how methods are incorporated in tasks and processes. In addition, it presents methods and theory, including content for emergent fields such as Sentiment Analysis and Network Alignment.  Other sections survey how Artificial Intelligence is exploited in bioinformatics applications, including sequence analysis, structure analysis, functional analysis, protein classification, omics analysis, biomarker discovery, integrative bioinformatics, protein interaction analysis, metabolic networks analysis, and much more.

• Bridges the gap between computer science and bioinformatics, combining an introduction to Artificial Intelligence methods with a systematic review of its applications in the life sciences
• Brings readers up-to-speed on current trends and methods in a dynamic and growing field
• Provides academic teachers with a complete resource, covering fundamental concepts as well as applications

Mario Cannataro is a Full Professor of computer engineering at the University 'Magna Græcia' of Catanzaro, Italy, and the Director of the Data Analytics Research Center. His current research interests include bioinformatics, health informatics, artificial intelligence, data mining, parallel computing. He published three books and more than 200 papers in international journals and conference proceedings. Mario Cannataro is a Senior Member of ACM and a Member of the Board of Directors of ACM SIGBio, a Senior Member of IEEE, a Member of IEEE Computer Society, and a Senior Member of BITS (Italian Bioinformatics Society).

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Artificial Intelligence in Bioinformatics: From Omics Analysis to Deep Learning and Network Mining reviews the main applications of the topic, from omics analysis to deep learning and network mining. The book includes a rigorous introduction on bioinformatics, also reviewing how methods are incorporated in tasks and processes. In addition, it presents methods and theory, including content for emergent fields such as Sentiment Analysis and Network Alignment. Other sections survey how Artificial Intelligence is exploited in bioinformatics applications, including sequence analysis, structure analysis, functional analysis, protein classification, omics analysis, biomarker discovery, integrative bioinformatics, protein interaction analysis, metabolic networks analysis, and much more. Bridges the gap between computer science and bioinformatics, combining an introduction to Artificial Intelligence methods with a systematic review of its applications in the life sciences Brings readers up-to-speed on current trends and methods in a dynamic and growing field Provides academic teachers with a complete resource, covering fundamental concepts as well as applications