Approach for solving multimodal problems using Genetic Algorithms with Grouped into Species optimized with Predator-Prey

Over recent years, Genetic Algorithms have proven to be an appropriate tool for solving certain problems. However, it does not matter if the search space has several valid solutions, as their classic approach is insufficient. To this end, the idea of dividing the individuals into species has been successfully raised. However, this solution is not free of drawbacks, such as the emergence of redundant species, overlapping or performance degradation by significantly increasing the number of individuals to be evaluated. This paper presents the implementation of a method based on the predator-prey technique, with the aim of providing a solution to the problem, as well as a number of examples to prove its effectiveness

An Application of Fish Detection Based on Eye Search with Artificial Vision and Artificial Neural Networks

[Abstract] A fish can be detected by means of artificial vision techniques, without human intervention or handling the fish. This work presents an application for detecting moving fish in water by artificial vision based on the detection of a fish′s eye in the image, using the Hough algorithm and a Feed-Forward network. In addition, this method of detection is combined with stereo image recording, creating a disparity map to estimate the size of the detected fish. The accuracy and precision of this approach has been tested in several assays with living fish. This technique is a non-invasive method working in real-time and it can be carried out with low cost. Furthermore, it could find application in aquariums, fish farm management and to count the number of fish which swim through a fishway. In a fish farm it is important to know how the size of the fish evolves in order to plan the feeding and when to be able to catch fish. Our methodology allows fish to be detected and their size and weight estimated as they move underwater, engaging in natural behavior.FEDER funds e Ministerio de Economía y Competitividad; CGL2012-34688Ministerio de Educación, Cultura y Deporte; BES-2013-063444Ministerio de Economía y Competitividad; BIA2017-86738-RBIOCAI; UNLC08-1E-002BIOCAI; UNLC13-13-3503European Regional Development Funds; ED431C 2018/49Accreditation, Structuring, and Improvement of Consolidated Research Units and Singular Centers; ED431G/01FEDER funds e Ministerio de Economía y Competitividad; CTQ2016-74881-

Texture analysis in gel electrophoresis images using an integrative kernel-based approach

[Abstract] Texture information could be used in proteomics to improve the quality of the image analysis of proteins separated on a gel. In order to evaluate the best technique to identify relevant textures, we use several different kernel-based machine learning techniques to classify proteins in 2-DE images into spot and noise. We evaluate the classification accuracy of each of these techniques with proteins extracted from ten 2-DE images of different types of tissues and different experimental conditions. We found that the best classification model was FSMKL, a data integration method using multiple kernel learning, which achieved AUROC values above 95% while using a reduced number of features. This technique allows us to increment the interpretability of the complex combinations of textures and to weight the importance of each particular feature in the final model. In particular the Inverse Difference Moment exhibited the highest discriminating power. A higher value can be associated with an homogeneous structure as this feature describes the homogeneity; the larger the value, the more symmetric. The final model is performed by the combination of different groups of textural features. Here we demonstrated the feasibility of combining different groups of textures in 2-DE image analysis for spot detection.Instituto de Salud Carlos III; PI13/00280United Kingdom. Medical Research Council; G10000427, MC_UU_12013/8Galicia. Consellería de Economía e Industria; 10SIN105004P

Distributed under Creative Commons CC-BY 4.0 OPEN ACCESS A methodology for the design of experiments in computational intelligence with multiple regression models

ABSTRACT The design of experiments and the validation of the results achieved with them are vital in any research study. This paper focuses on the use of different Machine Learning approaches for regression tasks in the field of Computational Intelligence and especially on a correct comparison between the different results provided for different methods, as those techniques are complex systems that require further study to be fully understood. A methodology commonly accepted in Computational intelligence is implemented in an R package called RRegrs. This package includes ten simple and complex regression models to carry out predictive modeling using Machine Learning and well-known regression algorithms. The framework for experimental design presented herein is evaluated and validated against RRegrs. Our results are different for three out of five state-of-the-art simple datasets and it can be stated that the selection of the best model according to our proposal is statistically significant and relevant. It is of relevance to use a statistical approach to indicate whether the differences are statistically significant using this kind of algorithms. Furthermore, our results with three real complex datasets report different best models than with the previously published methodology. Our final goal is to provide a complete methodology for the use of different steps in order to compare the results obtained in Computational Intelligence problems, as well as from other fields, such as for bioinformatics, cheminformatics, etc., given that our proposal is open and modifiable

Mejora continua de la calidad de la docencia a partir del análisis de los resultados de evaluación

El objetivo de cualquier docente debería ser la mejora continua en sus materias. En este trabajo se muestra una aproximación para adecuar las enseñanzas a aquellos aspectos más necesarios dentro de una materia. Para ello es necesario tomar nota de las debilidades mostradas por el alumnado. Por lo tanto, se plantea un análisis exhaustivo del rendimiento, más allá de una simple evaluación numérica, con el objetivo de dirigir los esfuerzos docentes a las áreas en las que se detecta una mayor necesidad. Así, para valorar los conocimientos teóricos se mostrará un análisis estadístico a partir de los resultados de la prueba teórica realizada (de tipo respuesta múltiple) analizando no sólo la cantidad de fallos sino analizando dónde y en qué porcentaje se producen éstos. En relación a la práctica, se desarrolla una rúbrica que permite una corrección exhaustiva de los trabajos, dejando además abierta la posibilidad a apuntar las observaciones necesarias en todos los puntos de interés. Se contextualiza la propuesta realizada en una materia concreta (Marcos de Desarrollo), puesto que es la materia que se empleó para su puesta en marcha. Sin embargo, el método propuesto es totalmente genérico y puede ser trasladado sin apenas cambio a cualquier otra materia.The objective of any teaching should be the continuous improvement of the subjects. This paper shows an approach to adapt the teachings to those aspects most necessary within a subject. For this, it is necessary to take note of the weaknesses shown by the students. Therefore, an exhaustive analysis of the performance is proposed, beyond a simple numerical evaluation, with the aim of directing the teaching efforts to the areas in which a greater need is detected. Thus, to assess theoretical knowledge, a statistical analysis will be shown based on the results of the theoretical test carried out (multiple response type) analyzing not only the number of failures but analyzing where and in what percentage these occur. In relation to the practice, a rubric is developed that allows an exhaustive correction of the works, leaving also open the possibility to record the necessary observations in all the points of interest. The proposal made in a specific subject (Development Frameworks) is contextualized, since it is the material used for its implementation. However, the proposed method is totally generic and can be transferred with little change to any other subject

Prediction of Antimalarial Drug-Decorated Nanoparticle Delivery Systems with Random Forest Models

Drug-decorated nanoparticles (DDNPs) have important medical applications. The current work combined Perturbation Theory with Machine Learning and Information Fusion (PTMLIF). Thus, PTMLIF models were proposed to predict the probability of nanoparticle–compound/drug complexes having antimalarial activity (against Plasmodium). The aim is to save experimental resources and time by using a virtual screening for DDNPs. The raw data was obtained by the fusion of experimental data for nanoparticles with compound chemical assays from the ChEMBL database. The inputs for the eight Machine Learning classifiers were transformed features of drugs/compounds and nanoparticles as perturbations of molecular descriptors in specific experimental conditions (experiment-centered features). The resulting dataset contains 107 input features and 249,992 examples. The best classification model was provided by Random Forest, with 27 selected features of drugs/compounds and nanoparticles in all experimental conditions considered. The high performance of the model was demonstrated by the mean Area Under the Receiver Operating Characteristics (AUC) in a test subset with a value of 0.9921 ± 0.000244 (10-fold cross-validation). The results demonstrated the power of information fusion of the experimental-centered features of drugs/compounds and nanoparticles for the prediction of nanoparticle–compound antimalarial activity. The scripts and dataset for this project are available in the open GitHub repository.This research and the APC were funded by Consolidation and Structuring of Competitive Research Units—Competitive Reference Groups (ED431C 2018/49) funded by the Ministry of Education, University and Vocational Training of Xunta de Galicia endowed with EU FEDER funds

Prediction of Anti-Glioblastoma Drug-Decorated Nanoparticle Delivery Systems Using Molecular Descriptors and Machine Learning

The theoretical prediction of drug-decorated nanoparticles (DDNPs) has become a very important task in medical applications. For the current paper, Perturbation Theory Machine Learning (PTML) models were built to predict the probability of different pairs of drugs and nanoparticles creating DDNP complexes with anti-glioblastoma activity. PTML models use the perturbations of molecular descriptors of drugs and nanoparticles as inputs in experimental conditions. The raw dataset was obtained by mixing the nanoparticle experimental data with drug assays from the ChEMBL database. Ten types of machine learning methods have been tested. Only 41 features have been selected for 855,129 drug-nanoparticle complexes. The best model was obtained with the Bagging classifier, an ensemble meta-estimator based on 20 decision trees, with an area under the receiver operating characteristic curve (AUROC) of 0.96, and an accuracy of 87% (test subset). This model could be useful for the virtual screening of nanoparticle-drug complexes in glioblastoma. All the calculations can be reproduced with the datasets and python scripts, which are freely available as a GitHub repository from authors. View Full-TextThe APC was funded by IKERDATA, S.L. under grant 3/12/DP/2021/00102—Area 1: Development of innovative business projects, from Provincial Council of Vizcaya (BEAZ for the Creation of Innovative Business Innovative business ventures)

Molecular phylogenetic analysis of the coccidian cephalopod parasites Aggregata octopiana and Aggregata eberthi (Apicomplexa: Aggregatidae) from the NE Atlantic coast using 18S rRNA sequences

8 páginas, 2 figuras, 1 tablaThe coccidia genus Aggregata is responsible for intestinal coccidiosis in wild and cultivated cephalopods. Two coccidia species, Aggregata octopiana, (infecting the common octopus Octopus vulgaris), and A. eberthi, (infecting the cuttlefish Sepia officinalis), are identified in European waters. Extensive investigation of their morphology resulted in a redescription of A. octopiana in octopuses from the NE Atlantic Coast (NW Spain) thus clarifying confusing descriptions recorded in the past. The present study sequenced the 18S rRNA gene in A. octopiana and A. eberthi from the NE Atlantic coast in order to assess their taxonomic and phylogenetic status. Phylogenetic analyses revealed conspecific genetic differences (2.5%) in 18S rRNA sequences between A. eberthi from the Ria of Vigo (NW Spain) and the Adriatic Sea. Larger congeneric differences (15.9%) were observed between A. octopiana samples from the same two areas, which suggest the existence of two species. Based on previous morphological evidence, host specificity data, and new molecular phylogenetic analyses, we suggest that A. octopiana from the Ria of Vigo is the valid type species.This work has been funded by a research grant from the Galician Council Xunta de Galicia (10PXIB402116PR)Peer reviewe

Approach for solving multimodal problems using Genetic Algorithms with Grouped into Species optimized with Predator-Prey

Over recent years, Genetic Algorithms have proven to be an appropriate tool for solving certain problems. However, it does not matter if the search space has several valid solutions, as their classic approach is insufficient. To this end, the idea of dividing the individuals into species has been successfully raised. However, this solution is not free of drawbacks, such as the emergence of redundant species, overlapping or performance degradation by significantly increasing the number of individuals to be evaluated. This paper presents the implementation of a method based on the predator-prey technique, with the aim of providing a solution to the problem, as well as a number of examples to prove its effectiveness