Overview

The field of biology and technology is constantly changing and growing. However, the abundance and intricacy of biological data present significant challenges for researchers, educators, and students. Deciphering this vast sea of information to extract meaningful insights can be difficult. Traditional approaches often fail to provide comprehensive solutions to these intricate problems, leaving many struggling to navigate the complexities of bioinformatics. Effective Techniques for Bioinformatic Exploration brings new clarity to the world of bioinformatics, offering a comprehensive solution to the challenges scholars face. Through its meticulously crafted chapters, this book provides a structured approach to understanding and applying bioinformatics principles. Bridging the gap between theory and practice equips readers with the tools needed to tackle complex biological problems effectively. Whether delving into genomics, proteomics, or machine learning models, this book offers a roadmap for success. This book empowers readers to overcome challenges and make meaningful contributions to the field by embracing the scientific method and showcasing the practical application of bioinformatics techniques.

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Paulo Fazendeiro received a PhD in Informatics Engineering (2010), held Pedagogical Aptitude and Scientific Capacity Tests (2001), and holds a Degree in Mathematics and Informatics (1995, best student). Both degrees were obtained at the University of Beira Interior, Portugal. He is currently an assistant professor at the Faculty of Engineering at the University of Beira Interior. He belongs to the Informatics Department where he teaches predominantly subjects in the areas of Artificial Intelligence, Bioinformatics and Programming. He is course director of the 2nd cycle of studies in Computer Science and Engineering (MSc), having previously served as director of the 1st cycle of studies (BSc) in Computer Science and Engineering and also of the 1st cycle of studies in Bioengineering. His research interests include Computational Intelligence, Granular Computing and Fuzzy Systems, Knowledge Discovery and Data Mining, Evolutionary Algorithms, as well as clustering techniques with applications for different areas. He is a member of the Pattern Recognition and Image Analysis group (PIA-Cv) of the Instituto de Telecomunicações (IT). Carmelina Leite has a PhD degree in Bioinformatics, Federal University of Minas Gerais, Brasil. She developed an algorithm for the ligand-based virtual screening based on linear algebra - The Milk-Way algorithm. This algorithm can be used not only to discover new drugs but also to reposition commercialized drugs. This structure-based screening resulted in a suggestion of treatment for COVID-19, the tetrachlorodecaoxide. Her dissertation comprised four years of work, giving rise to two deposit patents. She also holds an MSc in Bioinformatics and in Pharmaceutical Sciences. Currently, Carmelina is an entrepreneur in an innovation project of nutraceuticals and drug development using data mining techniques.

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