Description
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.
Author's/Editor's Biography
Paulo Fazendeiro (Ed.)
Paulo Fazendeiro received the Ph.D. in Computer Science and Engineering (2010), the equivalence of M.S. degree in Computer Science (2001), and the B.S. degree in Mathematics and Informatics (1995, with honors) all from the University of Beira Interior, Portugal. Currently he is an Assistant Professor at the Informatics Department of the University of Beira Interior. His research interests encompass Computational Intelligence and Granular Computing including the application of fuzzy set theory and fuzzy systems, knowledge discovery and data mining, evolutionary algorithms and their parallelization, multi-objective optimization and clustering techniques with applications to image processing. Dr. Fazendeiro is member of the Pattern and Image Analysis group of the Portuguese Telecommunications Institute and is also a member of the Informatics Laboratory of the University of Algarve.
Carmelina Leite (Ed.)
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. Currently, Carmelina is an entrepreneur in an innovation project of nutraceuticals and drug development using data mining techniques.