On the role of metaheuristic optimization in bioinformatics

Metaheuristic algorithms are employed to solve complex and large-scale optimization problems in many different fields, from transportation and smart cities to finance. This paper discusses how metaheuristic algorithms are being applied to solve different optimization problems in the area of bioinformatics. While the text provides references to many optimization problems in the area, it focuses on those that have attracted more interest from the optimization community. Among the problems analyzed, the paper discusses in more detail the molecular docking problem, the protein structure prediction, phylogenetic inference, and different string problems. In addition, references to other relevant optimization problems are also given, including those related to medical imaging or gene selection for classification. From the previous analysis, the paper generates insights on research opportunities for the Operations Research and Computer Science communities in the field of bioinformatics

Multi-objective community detection applied to social and COVID-19 constructed networks

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University LondonCommunity Detection plays an integral part in network analysis, as it facilitates understanding the structures and functional characteristics of the network. Communities organize real-world networks into densely connected groups of nodes. This thesis provides a critical analysis of the Community Detection and highlights the main areas including algorithms, evaluation metrics, applications, and datasets in social networks. After defining the research gap, this thesis proposes two Attribute-Based Label Propagation algorithms that maximizes both Modularity and homogeneity. Homogeneity is considered as an objective function one time, and as a constraint another time. To better capture the homogeneity of real-world networks, a new Penalized Homogeneity degree (PHd) is proposed, that can be easily personalized based on the network characteristics. For the first time, COVID-19 tracing data are utilized to form two dataset networks: one is based on the virus transition between the world countries. While the second dataset is an attributed network based on the virus transition among the contact-tracing in the Kingdom of Bahrain. This type of networks that is concerned in tracking a disease was not formed based on COVID-19 virus and has never been studied as a community detection problem. The proposed datasets are validated and tested in several experiments. The proposed Penalized Homogeneity measure is personalized and used to evaluate the proposed attributed network. Extensive experiments and analysis are carried out to evaluate the proposed methods and benchmark the results with other well-known algorithms. The results are compared in terms of Modularity, proposed PHd, and accuracy measures. The proposed methods have achieved maximum performance among other methods, with 26.6% better performance in Modularity, and 33.96% in PHd on the proposed dataset, as well as noteworthy results on benchmarking datasets with improvement in Modularity measures of 7.24%, and 4.96% respectively, and proposed PHd values 27% and 81.9%

Metaheurísticas, optimización multiobjetivo y paralelismo para descubrir motifs en secuencias de ADN

La resolución de problemas complejos mediante técnicas evolutivas es uno de los aspectos más investigados en Informática. El objetivo principal de esta tesis doctoral es desarrollar nuevos algoritmos capaces de resolver estos problemas con el menor tiempo computacional posible, mejorando la calidad de los resultados obtenidos por los métodos ya existentes. Para ello, combinamos tres conceptos importantes: metaheurísticas, optimización multiobjetivo y paralelismo. Con este fin, primero buscamos un problema de optimización importante que aún no fuese resuelto de forma eficiente y encontramos el Problema del Descubrimiento de Motifs (PDM). El PDM tiene como objetivo descubrir pequeños patrones repetidos (motifs) en conjuntos de secuencias de ADN que puedan poseer cierto significado biológico. Para abordarlo, definimos una formulación multiobjetivo adecuada a los requerimientos del mundo real, implementamos un total de diez algoritmos de distinta naturaleza (población, trayectoria, inteligencia colectiva...), analizando aspectos como la capacidad de escalar y converger. Finalmente, diseñamos diversas técnicas paralelas, haciendo uso de entornos de programación como OpenMP y MPI, que tratan de combinar las propiedades de varias metaheurísticas en una única aplicación. Los resultados obtenidos son estudiados en detalle a través de la aplicación de numerosos test estadísticos, y las predicciones son comparadas con las descubiertas por un total de trece herramientas biológicas bien conocidas en la literatura. Las conclusiones obtenidas demuestran que la utilización de la optimización multiobjetivo en técnicas metaheurísticas favorece el descubrimiento de soluciones de calidad y que el paralelismo es útil para combinar las propiedades evolutivas de diferentes algoritmos.The resolution of complex problems by using evolutionary algorithms is one of the most researched issues in Computer Science. The main goal of this thesis is directly related with the development of new algorithms that can solve this kind of problems with the least possible computational time, improving the results achieved by the existing methods. To this end, we combine three important concepts: metaheuristics, multiobjective optimization, and parallelism. For doing this, we first look for a significant optimization problem that had not been solved in an efficient way and we find the Motif Discovery Problem (MDP). MDP aims to discover over-represented short patterns (motifs) in a set of DNA sequences that may have some biological significance. To address it, we defined a multiobjective formulation adjusted to the real-world biological requirements, we implemented a total of ten algorithms of different nature (population, trajectory, collective intelligence...), analyzing aspects such as the ability to scale and converge. Finally, we designed parallel techniques, by using parallel and distributed programming environments as OpenMP and MPI, which try to combine the properties of several metaheuristics in a single application. The obtained results are discussed in detail through numerous statistical tests, and the achieved predictions are compared with those discovered by a total of thirteen well-known biological tools. The drawn conclusions demonstrate that using multiobjective optimization in metaheuristic techniques favors the discovery of quality solutions, and that parallelism is useful for combining the properties of different evolutionary algorithms.Ministerio de Economía y Competitividad - FEDER (TIN2008-06491-C04-04; TIN2012-30685) Gobierno de Extremadura (GR10025-TIC015

Time series data mining: preprocessing, analysis, segmentation and prediction. Applications

Currently, the amount of data which is produced for any information system is increasing exponentially. This motivates the development of automatic techniques to process and mine these data correctly. Specifically, in this Thesis, we tackled these problems for time series data, that is, temporal data which is collected chronologically. This kind of data can be found in many fields of science, such as palaeoclimatology, hydrology, financial problems, etc. TSDM consists of several tasks which try to achieve different objectives, such as, classification, segmentation, clustering, prediction, analysis, etc. However, in this Thesis, we focus on time series preprocessing, segmentation and prediction. Time series preprocessing is a prerequisite for other posterior tasks: for example, the reconstruction of missing values in incomplete parts of time series can be essential for clustering them. In this Thesis, we tackled the problem of massive missing data reconstruction in SWH time series from the Gulf of Alaska. It is very common that buoys stop working for different periods, what it is usually related to malfunctioning or bad weather conditions. The relation of the time series of each buoy is analysed and exploited to reconstruct the whole missing time series. In this context, EANNs with PUs are trained, showing that the resulting models are simple and able to recover these values with high precision. In the case of time series segmentation, the procedure consists in dividing the time series into different subsequences to achieve different purposes. This segmentation can be done trying to find useful patterns in the time series. In this Thesis, we have developed novel bioinspired algorithms in this context. For instance, for paleoclimate data, an initial genetic algorithm was proposed to discover early warning signals of TPs, whose detection was supported by expert opinions. However, given that the expert had to individually evaluate every solution given by the algorithm, the evaluation of the results was very tedious. This led to an improvement in the body of the GA to evaluate the procedure automatically. For significant wave height time series, the objective was the detection of groups which contains extreme waves, i.e. those which are relatively large with respect other waves close in time. The main motivation is to design alert systems. This was done using an HA, where an LS process was included by using a likelihood-based segmentation, assuming that the points follow a beta distribution. Finally, the analysis of similarities in different periods of European stock markets was also tackled with the aim of evaluating the influence of different markets in Europe. When segmenting time series with the aim of reducing the number of points, different techniques have been proposed. However, it is an open challenge given the difficulty to operate with large amounts of data in different applications. In this work, we propose a novel statistically-driven CRO algorithm (SCRO), which automatically adapts its parameters during the evolution, taking into account the statistical distribution of the population fitness. This algorithm improves the state-of-the-art with respect to accuracy and robustness. Also, this problem has been tackled using an improvement of the BBPSO algorithm, which includes a dynamical update of the cognitive and social components in the evolution, combined with mathematical tricks to obtain the fitness of the solutions, which significantly reduces the computational cost of previously proposed coral reef methods. Also, the optimisation of both objectives (clustering quality and approximation quality), which are in conflict, could be an interesting open challenge, which will be tackled in this Thesis. For that, an MOEA for time series segmentation is developed, improving the clustering quality of the solutions and their approximation. The prediction in time series is the estimation of future values by observing and studying the previous ones. In this context, we solve this task by applying prediction over high-order representations of the elements of the time series, i.e. the segments obtained by time series segmentation. This is applied to two challenging problems, i.e. the prediction of extreme wave height and fog prediction. On the one hand, the number of extreme values in SWH time series is less with respect to the number of standard values. In this way, the prediction of these values cannot be done using standard algorithms without taking into account the imbalanced ratio of the dataset. For that, an algorithm that automatically finds the set of segments and then applies EANNs is developed, showing the high ability of the algorithm to detect and predict these special events. On the other hand, fog prediction is affected by the same problem, that is, the number of fog events is much lower tan that of non-fog events, requiring a special treatment too. A preprocessing of different data coming from sensors situated in different parts of the Valladolid airport are used for making a simple ANN model, which is physically corroborated and discussed. The last challenge which opens new horizons is the estimation of the statistical distribution of time series to guide different methodologies. For this, the estimation of a mixed distribution for SWH time series is then used for fixing the threshold of POT approaches. Also, the determination of the fittest distribution for the time series is used for discretising it and making a prediction which treats the problem as ordinal classification. The work developed in this Thesis is supported by twelve papers in international journals, seven papers in international conferences, and four papers in national conferences

A data mining framework based on boundary-points for gene selection from DNA-microarrays: Pancreatic Ductal Adenocarcinoma as a case study

[EN] Gene selection (or feature selection) from DNA-microarray data can be focused on different techniques, which generally involve statistical tests, data mining and machine learning. In recent years there has been an increasing interest in using hybrid-technique sets to face the problem of meaningful gene selection; nevertheless, this issue remains a challenge. In an effort to address the situation, this paper proposes a novel hybrid framework based on data mining techniques and tuned to select gene subsets, which are meaningfully related to the target disease conducted in DNA-microarray experiments. For this purpose, the framework above deals with approaches such as statistical significance tests, cluster analysis, evolutionary computation, visual analytics and boundary points. The latter is the core technique of our proposal, allowing the framework to define two methods of gene selection. Another novelty of this work is the inclusion of the age of patients as an additional factor in our analysis, which can leading to gaining more insight into the disease. In fact, the results reached in this research have been very promising and have shown their biological validity. Hence, our proposal has resulted in a methodology that can be followed in the gene selection process from DNA-microarray data

Air Force Institute of Technology Research Report 2004

This report summarizes the research activities of the Air Force Institute of Technology’s Graduate School of Engineering and Management. It describes research interests and faculty expertise; lists student theses/dissertations; identifies research sponsors and contributions; and outlines the procedures for contacting the school. Included in the report are: faculty publications, conference presentations, consultations, and funded research projects. Research was conducted in the areas of Aeronautical and Astronautical Engineering, Electrical Engineering and Electro-Optics, Computer Engineering and Computer Science, Systems and Engineering Management, Operational Sciences, and Engineering Physics