Bioinformatics Applications Based On Machine Learning

The great advances in information technology (IT) have implications for many sectors, such as bioinformatics, and has considerably increased their possibilities. This book presents a collection of 11 original research papers, all of them related to the application of IT-related techniques within the bioinformatics sector: from new applications created from the adaptation and application of existing techniques to the creation of new methodologies to solve existing problems

Cophylogenetic analysis of dated trees

Parasites and the associations they form with their hosts is an important area of research due to the associated health risks which parasites pose to the human population. The associations parasites form with their hosts are responsible for a number of the worst emerging diseases impacting global health today, including Ebola, HIV, and malaria. Macro-scale coevolutionary research aims to analyse these associations to provide further insights into these deadly diseases. This approach, first considered by Fahrenholz in 1913, has been applied to hundreds of coevolutionary systems and remains the most robust means to infer the underlying relationships which form between coevolving species. While reconciling the coevolutionary relationships between a pair of evolutionary systems is NP-Hard, it has been shown that if dating information exists there is a polynomial solution. These solutions however are computationally expensive, and are quickly becoming infeasible due to the rapid growth of phylogenetic data. If the rate of growth continues in line with the last three decades, the current means for analysing dated systems will become computationally infeasible. Within this thesis a collection of algorithms are introduced which aim to address this problem. This includes the introduction of the most efficient solution for analysing dated coevolutionary systems optimally, along with two linear time heuristics which may be applied where traditional algorithms are no longer feasible, while still offering a high degree of accuracy 91%. Finally, this work integrates these incremental results into a single model which is able to handle widespread parasitism, the case where parasites infect multiple hosts. This proposed model reconciles two competing theories of widespread parasitism, while also providing an accuracy improvement of 21%, one of the largest single improvements provided in this field to date. As such, the set of algorithms introduced within this thesis offers another step toward a unified coevolutionary analysis framework, consistent with Fahrenholz original coevolutionary analysis model

Advances in Artificial Intelligence: Models, Optimization, and Machine Learning

The present book contains all the articles accepted and published in the Special Issue “Advances in Artificial Intelligence: Models, Optimization, and Machine Learning” of the MDPI Mathematics journal, which covers a wide range of topics connected to the theory and applications of artificial intelligence and its subfields. These topics include, among others, deep learning and classic machine learning algorithms, neural modelling, architectures and learning algorithms, biologically inspired optimization algorithms, algorithms for autonomous driving, probabilistic models and Bayesian reasoning, intelligent agents and multiagent systems. We hope that the scientific results presented in this book will serve as valuable sources of documentation and inspiration for anyone willing to pursue research in artificial intelligence, machine learning and their widespread applications

Statistical Population Genomics

This open access volume presents state-of-the-art inference methods in population genomics, focusing on data analysis based on rigorous statistical techniques. After introducing general concepts related to the biology of genomes and their evolution, the book covers state-of-the-art methods for the analysis of genomes in populations, including demography inference, population structure analysis and detection of selection, using both model-based inference and simulation procedures. Last but not least, it offers an overview of the current knowledge acquired by applying such methods to a large variety of eukaryotic organisms. Written in the highly successful Methods in Molecular Biology series format, chapters include introductions to their respective topics, pointers to the relevant literature, step-by-step, readily reproducible laboratory protocols, and tips on troubleshooting and avoiding known pitfalls. Authoritative and cutting-edge, Statistical Population Genomics aims to promote and ensure successful applications of population genomic methods to an increasing number of model systems and biological questions

Knowledge Discovery Models for Product Design, Assembly Planning and Manufacturing System Synthesis

The variety of products has been growing over the last few decades so that the challenges for designers and manufacturers to enhance their design and manufacturing capabilities, responsively and cost-effectively are greater than ever. The main objective of this research is to help designers and manufacturers cope with the increasing variety management challenges by exploiting the data records of existing or old products, along with appropriate Knowledge Discovery (KD) models, in order to extract the embedded knowledge in such data and use it to speed-up the development of new products. Four product development activities have been successfully addressed in this research: product design, product family formation, assembly sequencing and manufacturing system synthesis. The models and methods developed in this dissertation present a package of knowledge-based solutions that can greatly support product designers and manufacturers at various stages of the product development and manufacturing planning stages. For design retrieval; using efficient tree reconciliation algorithms found in Biological Sciences, a novel Bill of Materials (BOM) trees matching method was developed to retrieve the closest old design and discover components and structure shared with new product design. As a further application to BOM matching, an enhanced BOM matching method was also developed and used for product family formation. A new approach was introduced for assembly sequencing, based on the notion of consensus trees used in evolutionary studies, to overcome the critical limitation of individual assembly sequence retrieval methods that are not able to capture the assembly sequence data for a given new combination of components that never existed before in the same product variant. For manufacturing system synthesis; a novel Integer Programming model was developed to extract association rules between the product design domain and manufacturing domain to be used for synthesizing a manufacturing/assembly system for new products. Examples of real products were used to demonstrate and validate the developed models and comparisons with related existing methods were carried out to demonstrate the advantages of the developed models. The outcomes of this research provide efficient, and easy to implement knowledge-based solutions for facilitating cost-effective and rapid product development activities

Natural Computing and Beyond

This book contains the joint proceedings of the Winter School of Hakodate (WSH) 2011 held in Hakodate, Japan, March 15–16, 2011, and the 6th International Workshop on Natural Computing (6th IWNC) held in Tokyo, Japan, March 28–30, 2012, organized by the Special Interest Group of Natural Computing (SIG-NAC), the Japanese Society for Artificial Intelligence (JSAI). This volume compiles refereed contributions to various aspects of natural computing, ranging from computing with slime mold, artificial chemistry, eco-physics, and synthetic biology, to computational aesthetics

Cardinality constraints and dimensionality reduction in optimization problems

Tesis doctoral inédita. Universidad Autónoma de Madrid, Escuela Politécnica Superior, junio de 201