Artificial Intelligence in Bioinformatics

Artificial Intelligence in Bioinformatics

From Omics Analysis to Deep Learning and Network Mining

1st Edition - May 12, 2022

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  • Authors: Mario Cannataro, Pietro Guzzi, Giuseppe Agapito, Chiara Zucco, Marianna Milano
  • eBook ISBN: 9780128229293
  • Paperback ISBN: 9780128229521

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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.

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


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

  • Cover image
  • Title page
  • Table of Contents
  • Copyright
  • Dedication
  • About the authors
  • Preface
  • Why you should read this book now
  • Who should read this book
  • How this book is organized
  • Acknowledgments
  • Part 1: Artificial intelligence: methods
  • Introduction
  • Part 1 outline
  • Chapter 1: Knowledge representation and reasoning
  • Abstract
  • 1.1. Introduction
  • 1.2. Knowledge representation
  • 1.3. Reasoning
  • 1.4. Computer science and knowledge representation and reasoning
  • 1.5. Artificial intelligence and knowledge representation and reasoning
  • 1.6. Languages for knowledge representation and reasoning
  • 1.7. Artificial intelligence and bioinformatics
  • Bibliography
  • Chapter 2: Machine learning
  • Abstract
  • 2.1. Introduction
  • 2.2. Classification
  • 2.3. Clustering
  • 2.4. Association learning
  • 2.5. Reinforcement learning
  • Bibliography
  • Chapter 3: Artificial intelligence
  • Abstract
  • 3.1. A brief history of artificial intelligence
  • 3.2. Artificial intelligence and bioinformatics
  • 3.3. Artificial intelligence in medicine: a short tale
  • Bibliography
  • Chapter 4: Data science
  • Abstract
  • 4.1. Introduction
  • 4.2. A quick primer on data
  • 4.3. The data science process
  • 4.4. Languages for data science
  • 4.5. Low and no coding tools for data science
  • Bibliography
  • Chapter 5: Deep learning
  • Abstract
  • 5.1. Introduction
  • 5.2. Introducing basic principles behind deep learning
  • 5.3. Popular deep neural networks architecture
  • Bibliography
  • Chapter 6: Explainability of AI methods
  • Abstract
  • 6.1. Introduction
  • 6.2. Explainable models in machine learning
  • 6.3. Application of explainable AI in medicine
  • Bibliography
  • Chapter 7: Intelligent agents
  • Abstract
  • 7.1. Introduction
  • 7.2. Types of intelligent agents
  • 7.3. Agent-oriented programming frameworks
  • Bibliography
  • Part 2: Artificial intelligence: bioinformatics
  • Introduction
  • Part 2 outline
  • Chapter 8: Sequence analysis
  • Abstract
  • 8.1. Introduction
  • 8.2. String similarity methods
  • 8.3. Dynamic programming algorithm for edit distances
  • 8.4. Multi-parameterized edit distances
  • 8.5. Alignment free sequence comparison
  • Bibliography
  • Chapter 9: Structure analysis
  • Abstract
  • 9.1. Introduction
  • 9.2. Protein secondary structure prediction
  • 9.3. Tertiary structure prediction
  • Bibliography
  • Chapter 10: Omics sciences
  • Abstract
  • 10.1. Introduction
  • 10.2. Genomics
  • 10.3. Transcriptomics
  • 10.4. Epigenomics
  • 10.5. Proteomics
  • 10.6. Metabolomics
  • 10.7. Interactomics
  • 10.8. Gene prioritization
  • Bibliography
  • Chapter 11: Ontologies in bioinformatics
  • Abstract
  • 11.1. Introduction
  • 11.2. Biomedical ontologies
  • 11.3. Semantic similarity measures
  • 11.4. Functional enrichment analysis
  • Bibliography
  • Chapter 12: Integrative bioinformatics
  • Abstract
  • 12.1. Introduction
  • 12.2. Data integration in bioinformatics
  • 12.3. Databases, tools, and languages
  • Bibliography
  • Chapter 13: Biological networks analysis
  • Abstract
  • 13.1. Introduction
  • 13.2. Networks in biology
  • 13.3. Motif discovery
  • 13.4. Network embedding (representation learning)
  • 13.5. Networks alignment
  • Bibliography
  • Chapter 14: Biological pathway analysis
  • Abstract
  • 14.1. Introduction
  • 14.2. Biological pathways
  • 14.3. Pathway databases
  • 14.4. Pathway representation formats
  • 14.5. Pathways enrichment analysis methods
  • 14.6. Pathway enrichment analysis tools
  • Bibliography
  • Chapter 15: Knowledge extraction from biomedical texts
  • Abstract
  • 15.1. Introduction
  • 15.2. A primer on text analysis
  • 15.3. Biomedical text mining tasks
  • Bibliography
  • Chapter 16: Artificial intelligence in bioinformatics: issues and challenges
  • Abstract
  • 16.1. Introduction
  • 16.2. Evolution of bioinformatics
  • 16.3. Challenges for artificial intelligence in bioinformatics
  • Bibliography
  • Appendix A: Python code examples
  • A.1. Classification of omics data
  • A.2. Cluster analysis of gene expression data
  • A.3. Python agent-oriented programming framework
  • A.4. Sequences similarity score calculation
  • A.5. Dynamic programming
  • A.6. Analysis of FASTQ sequences
  • A.7. Analysis of alignment map in SAM/BAM format
  • A.8. Mass spectrometer data analysis
  • Bibliography
  • Appendix B: Java code examples
  • B.1. Java agent-oriented programming frameworks
  • Bibliography
  • Bibliography
  • Index

Product details

  • No. of pages: 268
  • Language: English
  • Copyright: © Elsevier 2022
  • Published: May 12, 2022
  • Imprint: Elsevier
  • eBook ISBN: 9780128229293
  • 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 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

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