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 Graecia 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). Pietro Hiram Guzzi the Ph.D. degree in biomedical engi- neering from Magna Graecia University, Italy, in 2008. He has been an Associate Professor of computer engineering with Magna Graecia 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. Giuseppe Agapito is an assistant professor of computer engineering with the University Magna Graecia, 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. 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. 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).
Title: Artificial Intelligence in Bioinformatics: From Omics Analysis to Deep Learning and Network Mining
Author: Milano, Marianna,Zucco, Chiara,Agapito, Giuseppe,Guzzi, Pietro Hiram,Cannataro, Mario
ISBN: 9780128229521
Binding:
Publisher: Elsevier Science Publishing Co Inc
Publication Date: 2022-05-18
Number of Pages: 268
Weight: 0.5492 kg
