COVID-19 Update: We are currently shipping orders daily. However, due to transit disruptions in some geographies, deliveries may be delayed. To provide all customers with timely access to content, we are offering 50% off Science and Technology Print & eBook bundle options. Terms & conditions.
Artificial Intelligence in Bioinformatics - 1st Edition - ISBN: 9780128229521

Artificial Intelligence in Bioinformatics

1st Edition

From Omics Analysis to Deep Learning and Network Mining

0.0 star rating Write a review
Authors: Mario Cannataro Pietro Hiram Guzzi Giuseppe Agapito Chiara Zucco Marianna Milano
Paperback ISBN: 9780128229521
Imprint: Elsevier
Published Date: 1st April 2021
Page Count: 250
Sales tax will be calculated at check-out Price includes VAT/GST

Institutional Subscription

Secure Checkout

Personal information is secured with SSL technology.

Free Shipping

Free global shipping
No minimum order.

Description

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.

Key Features

  • 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

Readership

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

Table of Contents

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
a. Ontologies
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
20. Pharmacogenomics
21. Sentiment Analysis (for Telemedicine applications)
22. Topic Extractions (in Psychology)
23. Functional Enrichment Analysis

Details

No. of pages:
250
Language:
English
Copyright:
© Elsevier 2021
Published:
1st April 2021
Imprint:
Elsevier
Paperback ISBN:
9780128229521

About the Authors

Mario Cannataro

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).

Affiliations and Expertise

Professor of Computer Engineering, University Magna Graecia of Catanzaro, Catanzaro, Italy

Pietro Hiram Guzzi

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.

Affiliations and Expertise

Assocaite Professor of Computer Engineering, University of Magna Græcia, Catanzaro, Italy

Giuseppe Agapito

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.

Affiliations and Expertise

Assistant Professor of Computer Engineering, University Magna Graecia of Catanzaro, Catanzaro, Italy

Chiara Zucco

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.

Affiliations and Expertise

University Magna Graecia of Catanzaro, Catanzaro, Italy

Marianna Milano

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).

Affiliations and Expertise

University Magna Graecia of Catanzaro, Catanzaro, Italy

Ratings and Reviews