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…mehr
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.
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).
Inhaltsangabe
PART 1 ARTIFICIAL INTELLIGENCE: METHODS 1. Knowledge Representation and Reasoning 2. Machine Learning 3. Artificial Intelligence 4. Data Science 5. Deep Learning 6. Explainability of AI methods 7. Intelligent Agents
PART 2 ARTIFICIAL INTELLIGENCE: BIOINFORMATICS 8. Sequence Analysis 9. Structure Analysis 10. Omics Sciences 11. Ontologies in Bioinformatics 12. Integrative Bioinformatics 13. Biological Networks Analysis 14. Biological Pathway Analysis 15. Knowledge Extraction from Biomedical Texts 16. Artificial Intelligence in Bioinformatics: Issues and Challenges
PART 1 ARTIFICIAL INTELLIGENCE: METHODS 1. Knowledge Representation and Reasoning 2. Machine Learning 3. Artificial Intelligence 4. Data Science 5. Deep Learning 6. Explainability of AI methods 7. Intelligent Agents
PART 2 ARTIFICIAL INTELLIGENCE: BIOINFORMATICS 8. Sequence Analysis 9. Structure Analysis 10. Omics Sciences 11. Ontologies in Bioinformatics 12. Integrative Bioinformatics 13. Biological Networks Analysis 14. Biological Pathway Analysis 15. Knowledge Extraction from Biomedical Texts 16. Artificial Intelligence in Bioinformatics: Issues and Challenges
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