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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
Students and researchers in biomedicine and life science, working on bioinformatics, systems biology, molecular biology and biotechnology; computer scientists and engineers working on artificial intelligence methods and their applications in bioinformatics
PART I Artificial Intelligence Methods
1. Artificial Intelligence
2. Machine Learning
3. Knowledge and Reasoning
4. Data Mining
5. Text Mining
6. Data Science
7. Neural Networks
a. Convolutional Neural Networks
b. Deep Neural Networks
8. Intelligent Agents
9. Emergent issues
a. Consciousness of Ai Algorithms
b. Explainability of AI methods
PART II Artificial Intelligence in Bioinformatics
10. Sequence analysis
11. Structure analysis
12. Functional analysis
b. Semantic similarity measures
c. Protein classification
13. Omics analysis
14. Integrative bioinformatics
15. Metabolic networks analysis
a. Networks alignment
b. Network embedding
c. Protein interaction networks analysis
d. Pathways analysis
16. Reasoning in bioinformatics
17. Explainable models in bioinformatics
18. Knowledge extraction from scientific texts
PART III Emerging Applications of AI-Boosted Bioinformatics in Life Sciences
19. Biomarker discovery
21. Sentiment Analysis (for Telemedicine applications)
22. Topic Extractions (in Psychology)
23. Functional Enrichment Analysis
- No. of pages:
- © Elsevier 2021
- 1st October 2021
- Paperback ISBN:
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).
Professor of Computer Engineering, University Magna Graecia of Catanzaro, Catanzaro, Italy
Pietro Hiram Guzzi the Ph.D. degree in biomedical engi- neering from Magna Græcia University, Italy, in 2008. He has been an Associate Professor of computer engineering with Magna Græcia Univer- sity since 2008. He has been a Visiting Researcher with Georgia Tech University, Atlanta. He has authored two books. His research interests include semantic-based and network-based analysis of biological and clinical data. He is a member of the ACM, BITS, ISMB, and NETBIO COSI. He is an Editor of a newsletter of the ACM Special Interest Group on Bioinformatics, Computational Biology, and Biomedical Informatics (SIGBio), and the IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS. He serves the scientific community as a reviewer for many conferenceS. He wrote two books and he edited another one.
Assocaite Professor of Computer Engineering, University of Magna Græcia, Catanzaro, Italy
Giuseppe Agapito is an assistant professor of computer engineering with the University Magna Græcia, Catanzaro, Italy. His current research interests include analysis and visualization of biological networks, efficient analysis of genomics data, parallel computing, and data mining. In particular, the research activity is focused on the development and implementation of statistical and data mining methodologies also based on parallel and distributed computing, for the efficient analysis of omics data. He has published over 70 articles for international journals and conference proceedings. He is a member of the ACM, ACM SIGBio, and BITS.
Assistant Professor of Computer Engineering, University Magna Graecia of Catanzaro, Catanzaro, Italy
Chiara Zucco received her Master Degree in Mathematics at the University of Calabria. She currently is a third-year Ph.D. student in the Biomarkers of Chronic and Complex Diseases Ph.D. Program at University “Magna Graecia” of Catanzaro, Italy. Her Ph.D. research is mainly focused on applying Text Mining and in particular Sentiment Analysis techniques for patient monitoring and adverse events prediction. She is also interested in Explainable Artificial Intelligence.
University Magna Graecia of Catanzaro, Catanzaro, Italy
Marianna Milano received her Master Degree in Computer Engineering from the University "Magna Graecia" of Catanzaro, Italy, in 2011 and the Ph.D. degree in Biomarkers of Chronic and Complex Diseases at the University "Magna Graecia" of Catanzaro, Italy, in 2019. Her research interests comprise semantic-based and network-based analysis of biological and clinical data. She is a member of BITS (Italian Bioinformatics Society).
University Magna Graecia of Catanzaro, Catanzaro, Italy
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