Artificial Intelligence in Bioinformatics

From Omics Analysis to Deep Learning and Network Mining

1st Edition - May 12, 2022
Authors: Mario Cannataro, Pietro Hiram Guzzi, Giuseppe Agapito, Chiara Zucco, Marianna Milano
Language: English
Paperback ISBN:
9 7 8 - 0 - 1 2 - 8 2 2 9 5 2 - 1
eBook ISBN:
9 7 8 - 0 - 1 2 - 8 2 2 9 2 9 - 3

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… Read more

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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.

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

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