This book introduces characteristic features of the protein structure prediction (PSP) problem. It focuses on systematic selection and improvement of the most appropriate metaheuristic algorithm to solve the problem based on a fitness landscape analysis, rather than on the nature of the problem, which was the focus of methodologies in the past. Protein structure prediction is concerned with the question of how to determine the three-dimensional structure of a protein from its primary sequence. Recently a number of successful metaheuristic algorithms have been developed to determine the native structure, which plays an important role in medicine, drug design, and disease prediction. This interdisciplinary book consolidates the concepts most relevant to protein structure prediction (PSP) through global non-convex optimization. It is intended for graduate students from fields such as computer science, engineering, bioinformatics and as a reference for researchers and practitioners.

Meta-heuristic algorithms give a satisfactory solution of complex optimization problems in a reasonable time. They are among the most promising and successful optimization techniques. However, some problems are highly complex and require improved techniques. A careful analysis of the existing meta-heuristic algorithms and hybridization among them may facilitate the research in this direction. To test this hypothesis, the author of the thesis developed a computational tool using a few meta-heuristic algorithms where these algorithms can be analyzed in detail and possible hybridization among them can be created. As a case study, the tool is developed for simplified protein structure prediction. The proper working of the software is demonstrated by optimizing the two sets of standard benchmark sequences. Along with testing and analyzing meta-heuristic algorithms, the tool can be used for simplified protein structure prediction.

The importance of knowing the three dimensional structure of proteins and the difficulty of determining it experimentally, have led scientists to develop several computational methods for protein structure prediction. Despite the abundance of protein structure prediction methods, these approaches have two major limitations in additions to others. First, the top ranked 3d-model reported by a prediction server is not necessarily the best predicted 3d-model. The correct predicted 3d-model may be ranked within the top 10 predictions after some false positives. Second, no single method can give correct predictions for all proteins. To attempt to remedy these limitations, protein structure prediction "meta" approaches have been developed. Some meta-servers apply a local model quality assessment program (MQAP) to select a set of candidate 3d-models by ranking 3d-models obtained from other servers. However, model quality assessment programs suffer from the same two limitations as the prediction servers. The data available for training machine-learning-based meta-approaches is constantly growing in size on a monthly or a weekly basis. Once new data become available which may contain new patterns, typically one will discard the models trained on the old training data and train new ones. Clearly such an approach is a waste of computation and needs manual human intervention to retrain the learning algorithm. My research has three goals, (i) to invent a novel machine-learning based meta-MQAP; (ii) to develop a new meta-selector based on the meta-MQAP; (iii) to devise new machine learning algorithms that can extend my meta-MQAP-meta-selector to make use of the newly available labeled data dynamically. To that end, (i) I have developed a new meta-MQAP-meta-selector based a on a three-levels-hierarchy of general linear models; (ii) I have proposed two algorithms to handle the problem of the constantly growing training date. The first algorithm trains a model dynamically on the related data to the unlabeled query (testing) data, in another words, it trains dynamically a custom-made expert. The second algorithm dynamically mixes local experts which are already trained and cached. My experimental results show that my meta-MQAP outperforms the best of the tested model quality assessment program by 7%-8% in the overall score. When selecting from the predictions made by humans in a standard benchmark CASP7, my meta-selectors achieve about 3% improvement above the best human predictor. I have participated in the world wide CASP8 competition with three meta-MQAP-meta-selectors. Based on the evaluation of 46 target proteins used in the recently completed, truly blind and independent CASP8 experiment, my meta-MQAP outperforms the best tested MQAP by 6%, 5%, 29%, and 10% in the easy, medium, hard categories and in the overall score respectively. These results show that my meta-MQAP outperforms any of its components proving that a hierarchy of weighted sums of the MQAP's scores has more information than a single MQAP. The three meta-selectors performances are very similar to the performance of the best performing CASP8 server, demonstrating that the "meta" approach used here, namely, meta-MQAPmeta-selection, is promising and further improvements are likely to result in a significant improvement in the performance over the best of the servers.

This book includes high-quality research papers presented at the Fourth International Conference on Innovative Computing and Communication (ICICC 2021), which is held at the Shaheed Sukhdev College of Business Studies, University of Delhi, Delhi, India, on February 20–21, 2021. Introducing the innovative works of scientists, professors, research scholars, students and industrial experts in the field of computing and communication, the book promotes the transformation of fundamental research into institutional and industrialized research and the conversion of applied exploration into real-time applications.

This book presents best selected papers presented at the First Global Conference on Artificial Intelligence and Applications (GCAIA 2020), organized by the University of Engineering & Management, Jaipur, India, during 8–10 September 2020. The proceeding will be targeting the current research works in the domain of intelligent systems and artificial intelligence.