Measuring the similarity of protein structures by means of the universal similarity metric

Motivation: As an increasing number of protein structures become available, the need for algorithms that can quantify the similarity between protein structures increases as well. Thus, the comparison of proteins’ structures, and their clustering accordingly to a given similarity measure, is at the core of today’s biomedical research. In this paper, we show how an algorithmic information theory inspired Universal Similarity Metric (USM) can be used to calculate similarities between protein pairs.The method, besides being theoretically supported, is surprisingly simple to implement and computationally efficient. Results: Structural similarity between proteins in four different datasets was measured using the USM.The sample employed represented alpha, beta, alpha–beta, tim–barrel, globins and serpine protein types. The use of the proposed metric allows for a correct measurement of similarity and classification of the proteins in the four datasets. Availability: All the scripts and programs used for the preparation of this paper are available at http://www.cs.nott.ac.uk/ ~nxk/USM/protocol.html. In that web-page the reader will find a brief description on how to use the various scripts and programs.TIC2002-04242-C03-0

Algorithm portfolio based scheme for dynamic optimization problems

Since their first appearance in 1997 in the prestigious journal Science, algorithm portfolios have become a popular approach to solve static problems. Nevertheless and despite that success, they have not received much attention in Dynamic Optimization Problems (DOPs). In this work, we aim at showing these methods as a powerful tool to solve combinatorial DOPs. To this end, we propose a new algorithm portfolio for this type of problems that incorporates a learning scheme to select, among the metaheuristics that compose it, the most appropriate solver or solvers for each problem, configuration and search stage. This method was tested over 5 binary-coded problems (dynamic variants of OneMax, Plateau, RoyalRoad, Deceptive and Knapsack) and compared versus two reference algorithms for these problems (Adaptive Hill Climbing Memetic Algorithm and Self Organized Random Immigrants Genetic Algorithm). The results showed the importance of a good design of the learning scheme, the superiority of the algorithm portfolio against the isolated version of the metaheuristics that integrate it, and the competitiveness of its performance versus the reference algorithms.Spanish Government TIN2011-27696-C02-01 TEC2013-45585-C2-2-RAndalusian Government P11-TIC-8001European CommissionBasque Government PC2013-71AIbero-American University Association for Post Graduate Studies (AUIP

Artificial intelligence and decision problems: The need for an ethical context

Computers process information and make decisions. Until recently, the decisions they made were not complex, but due to the incessant technological advances that are taking place, systems based on artificial intelligence are achieving levels of competence in decision-making that in many contexts equal or surpass those of humans. These are autonomous decision-making systems that, although they can increase the capacity and efficiency of people in their fields of action, they could also replace them, something that is of concern to society as a whole. Avoiding dysfunctions in these systems is a priority social, scientific and technological objective, which requires theoretical models that include all the richness and variety of decision problems, that precisely define the elements that characterize them and that address the ethical principles that should guide their operation. This article describes each of these aspects in separate sections

PRoA: An intelligent multi-criteria Personalized Route Assistant

Personalization of pedestrian routes becomes a necessity due to the wide variety of user profiles that may differ on preferences or requirements to choose a route. Several software applications offer routes usually based on single criterion like distance or time; however, these criteria do not often fit the pedestrian needs. Here, we will first focus on the Personalized Routes Problem and then we will approach the specific case of designing accessible and green pedestrian routes. The proposal is implemented as a freely available Android application (named as PRoA, by intelligent multi-criteria Personalized Route Assistant), which automatically obtains geographical data and information for the decision criteria from open datasets. The proposal is evaluated using real cases at the city of Granada, Spain.Research supported in part by projects TIN2014-55024-P (Spanish Ministry of Economy and Competitiveness) and P11-TIC-8001 (Consejería de Economía, Innovación y Ciencia, Junta de Andalucía). Both projects include FEDER funds from the European Union. M. Torres enjoys a Ph.D. research training staff grant associated with the project TIN2014-55024-P and co-funded by the European Social Fund

A functional programming approach to a computational biology problem

Protein Folding is an important open problem in the eld of Computational Biology Due to its com binatorial nature exact polynomial algorithms to solve it could not exist and so approximation algorithms and heuristics has to be used In this paper a new heuristic is studied based on the approach that considers that the folding process is coded into the protein One important aspect of this work is that the algorithm was implemented using functional programming resulting in advantages for the understanding of the problem The results obtained are comparable with the ones obtained for classical algorithms .Eje: Conferencia latinoamericana de programación funcionalRed de Universidades con Carreras en Informática (RedUNCI

The Role of Metaheuristics as Solutions Generators

Optimization problems are ubiquitous nowadays. Many times, their corresponding computational models necessarily leave out of consideration several characteristics and features of the real world, so trying to obtain the optimum solution can not be enough for a problem solving point of view. The aim of this paper is to illustrate the role of metaheuristics as solutions’ generators in a basic problem solving framework. Metaheuristics become relevant in two modes: firstly because every run (in the case of population based techniques) allows to obtain a set of potentially good solutions, and secondly, if a reference solution is available, one can set up a new optimization problem that allows to obtain solutions with similar quality in the objectives space but maximally different structure in the design space. Once a set of solutions is obtained, an example of an a posteriori analysis to rank them according with decision maker’s preferences is shown. All the problem solving framework steps, emphasizing the role of metaheuristics are illustrated with a dynamic version of the tourist trip design problem (for the first mode), and with a perishable food distribution problem (for the second one). These examples clearly show the benefits of the problem solving framework proposed. The potential role of the symmetry concept is also exploredProject PID2020-112754GB-I00 from MCINAEI/10.13039/ 501100011033

Optimisation problems as decision problems: The case of fuzzy optimisation problems

The importance that decision-making problems and optimisation problems have today in all aspects of life is beyond all doubt. Despite that importance, both problems tend to be thought of as following different routes, when they have, in fact, a “symbiotic” relation. Here, we consider the different decision problems that arise when different kinds of information and framework of behaviour are considered, and we explore the corresponding optimisation problems that can be derived for searching the best possible decision. We explore the case where Fuzzy Mathematical Programming problems are obtained as well as other new ones in the fuzzy context.Research supported by the project TIN2014-55024-P from the Spanish Govern as well as by the project TIC-8001 from the Andalusian Govern (both financed with FEDER funds)

Aplicación de técnicas evolutivas para el problema de plegado de proteínas

La Biología Molecular se dedica fundamentalmente al estudio de la estructura y funcionalidad de proteínas y ácidos nucleicos. A partir del descubrimiento de la estructura en doble hélice del ADN en 1953, el área ha tenido notorios avances. El volumen de información generado a partir de la manipulación de secuencias biomoleculares y la creciente potencia de las computadoras para realizar simulaciones de procesos biológicos complejos, han provocado que los Biológos Moleculares deban interactuar con sus pares de las Ciencias de la Computación y las Matemáticas para poder aprovechar la información generada. Como consecuencia de esta interacción surge la Biología Computacional: área que involucra el desarrollo y uso de técnicas matemáticas y de computación para facilitar el tratamiento de los problemas derivados de la Biología Molecular. Como ejemplo de este trabajo interdisciplinario podemos citar la aplicación de técnicas de bases de datos para almacenar la creciente cantidad de secuencias moleculares descubiertas, y que a través de Internet pueden consultarse, compararse para buscar similitudes y/o diferencias, etc. Por otro lado, en los últimos 30 años ha existido un creciente interés en el desarrollo de técnicas computacionales para la resolución de problemas complejos, que se basan en la utilización de los principios de evolución y herencia.Tesis digitalizada en SEDICI gracias a la colaboración de la Biblioteca de la Facultad de Informática.Facultad de Ciencias Exacta

A Metaheuristic Based Approach for the Customer-Centric Perishable Food Distribution Problem

The CNRST has awarded H. El Raoui an excellence scholarship. D. Pelta acknowledges support from projects TIN2017-86647-P (Spanish Ministry of Economy, Industry, and Competitiveness. Including FEDER funds) and PID2020-112754GB-I00 (Spanish Ministry of Science and Innovation).High transportation costs and poor quality of service are common vulnerabilities in various logistics networks, especially in food distribution. Here we propose a many-objective Customercentric Perishable Food Distribution Problem that focuses on the cost, the quality of the product, and the service level improvement by considering not only time windows but also the customers’ target time and their priority. Recognizing the difficulty of solving such model, we propose a General Variable Neighborhood Search (GVNS) metaheuristic based approach that allows to efficiently solve a subproblem while allowing us to obtain a set of solutions. These solutions are evaluated over some non-optimized criteria and then ranked using an a posteriori approach that requires minimal information about decision maker preferences. The computational results show (a) GVNS achieved same quality solutions as an exact solver (CPLEX) in the subproblem; (b) GVNS can generate a wide number of candidate solutions, and (c) the use of the a posteriori approach makes easy to generate different decision maker profiles which in turn allows to obtain different rankings of the solutions.CNRSTSpanish Ministry of Economy, Industry, and Competitiveness TIN2017-86647-PEuropean Commission TIN2017-86647-PSpanish Government PID2020-112754GB-I0