Algorithmic and Artificial Intelligence Methods for Protein Bioinformatics

Preface

Proteins are any of a group of complex organic macromolecules that contain carbon, hydrogen, oxygen, nitrogen, and usually sulfur and are composed of one or more chains of amino acids. Proteins are fundamental components of all living cells and include many substances, such as enzymes, hormones, and antibodies, which are essential for the proper functioning of an organism. Protein bioinformatics is a newer name for an already existing discipline. It encompasses the techniques and methodologies used in bioinformatics that are related to proteins. Proteins can be described as a sequence, a two-dimensional (2D) structure, or a three-dimensional (3D) structure. In addition, interactions among proteins can be described as a network or a graph. Hence, many traditional algorithmic techniques such as graph algorithms, heuristic algorithms, approximate algorithms, parameterized algorithms, and linear programming can be applied to analyze protein interaction networks. On the other hand, because of the large amount of data available from wet labs and experiments with proteins, traditional algorithmic methods may not be sufficiently powerful and intelligent to be applied. Hence, we can use many mature machine learning or artificial intelligence (AI) methods to analyze protein data such as predicting protein structures based on existing databases or datasets. These AI techniques include support vector machines (SVMs), hidden Markov models (HMMs), neural networks, decision trees, reinforcement ...