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Objective: Non-alcoholic fatty liver disease (NAFLD) represents the most common liver disease worldwide. The Latin American population has the highest obesity rates in the world. The aim was to research the factors associated with NAFLD in young adults. Methods: A cross-sectional study was performed on 171 participants with an average age of 21 years who underwent a physical and laboratory examination, anthropometric evaluation, and abdominal ultrasound. Results: Subjects with NAFLD were significantly overweight, with dyslipidemia, and with atherogenic risk. They had the presence of metabolic syndrome compared to those without NAFLD. Bivariate logistic regression showed that body mass index, atherogenic risk, Homeostatic Model Assessment (HOMA) value, and metabolic syndrome were associated with NAFLD development. Conclusions: The variables of HOMA, metabolic syndrome, and atherogenic risk were most associated as risk predictors of this pathology in young adults.

Estimation of COVID-19 dynamics and its evolution is a multidisciplinary effort, which requires the unification of heterogeneous disciplines (scientific, mathematics, epidemiological, biological/bio-chemical, virologists and health disciplines to mention the most relevant) to work together in a better understanding of this pandemic. Time series analysis is of great importance to determine both the similarity in the behavior of COVID-19 in certain countries/states and the establishment of models that can analyze and predict the transmission process of this infectious disease. In this contribution, an analysis of the different states of the United States will be carried out to measure the similarity of COVID-19 time series, using dynamic time warping distance (DTW) as a distance metric. A parametric methodology is proposed to jointly analyze infected and deceased persons. This metric allows to compare time series that have a different time length, making it very appropriate for studying the United States, since the virus did not spread simultaneously in all the states/provinces. After a measure of the similarity between the time series of the states of United States was determined, a hierarchical cluster was created, which makes it possible to analyze the behavioral relationships of the pandemic between different states and to discover interesting patterns and correlations in the underlying data of COVID-19 in the United States. With the proposed methodology, nine different clusters were obtained, showing a different behavior in the eastern zone and western zone of the United States. Finally, to make a prediction of the evolution of COVID-19 in the states, Logistic, Gompertz and SIR model was computed. With these mathematical model it is possible to have a more precise knowledge of the evolution and forecast of the pandemic.

We want to thank the great work done by the reviewers of each of the papers, together with the great interest shown by the editorial of BMC Bioinformatics in IWBBIO Conference. Special thanks to D. Omar El Bakry for his interest and great help to make this Special Issue. Thank the Ministry of Spain for the economic resources within the project with reference RTI2018-101674-B-I00., In the current supplement, we are proud to present seventeen relevant contributions from the 6th International Work-Conference on Bioinformatics and Biomedical Engineering (IWBBIO 2018), which was held during April 25-27, 2018 in Granada (Spain). These contributions have been chosen because of their quality and the importance of their findings., This research has been partially supported by the proyects with reference RTI2018-101674-B-I00 (Ministry of Spain) and B-TIC-414-UGR18 (FEDER, Junta Andalucia and UGR).

Many clinical studies have revealed the high biological similarities existing among different skin pathological states. These similarities create difficulties in the efficient diagnosis of skin cancer, and encourage to study and design new intelligent clinical decision support systems. In this sense, gene expression analysis can help find differentially expressed genes (DEGs) simultaneously discerning multiple skin pathological states in a single test. The integration of multiple heterogeneous transcriptomic datasets requires different pipeline stages to be properly designed: from suitable batch merging and efficient biomarker selection to automated classification assessment. This article presents a novel approach addressing all these technical issues, with the intention of providing new sights about skin cancer diagnosis. Although new future efforts will have to be made in the search for better biomarkers recognizing specific skin pathological states, our study found a panel of 8 highly relevant multiclass DEGs for discerning up to 10 skin pathological states: 2 healthy skin conditions a priori, 2 cataloged precancerous skin diseases and 6 cancerous skin states. Their power of diagnosis over new samples was widely tested by previously well-trained classification models. Robust performance metrics such as overall and mean multiclass F1-score outperformed recognition rates of 94% and 80%, respectively. Clinicians should give special attention to highlighted multiclass DEGs that have high gene expression changes present among them, and understand their biological relationship to different skin pathological states.

In more recent years, a significant increase in the number of available biological experiments has taken place due to the widespread use of massive sequencing data. Furthermore, the continuous developments in the machine learning and in the high performance computing areas, are allowing a faster and more efficient analysis and processing of this type of data. However, biological information about a certain disease is normally widespread due to the use of different sequencing technologies and different manufacturers, in different experiments along the years around the world. Thus, nowadays it is of paramount importance to attain a correct integration of biologically-related data in order to achieve genuine benefits from them. For this purpose, this work presents an integration of multiple Microarray and RNA-seq platforms, which has led to the design of a multiclass study by collecting samples from the main four types of leukemia, quantified at gene expression. Subsequently, in order to find a set of differentially expressed genes with the highest discernment capability among different types of leukemia, an innovative parameter referred to as coverage is presented here. This parameter allows assessing the number of different pathologies that a certain gen is able to discern. It has been evaluated together with other widely known parameters under assessment of an ANOVA statistical test which corroborated its filtering power when the identified genes are subjected to a machine learning process at multiclass level. The optimal tuning of gene extraction evaluated parameters by means of this statistical test led to the selection of 42 highly relevant expressed genes. By the use of minimum- Redundancy Maximum-Relevance (mRMR) feature selection algorithm, these genes were reordered and assessed under the operation of four different classification techniques. Outstanding results were achieved by taking exclusively the first ten genes of the ranking into consideration. Finally, specific literature was consulted on this last subset of genes, revealing the occurrence of practically all of them with biological processes related to leukemia. At sight of these results, this study underlines the relevance of considering a new parameter which facilitates the identification of highly valid expressed genes for simultaneously discerning multiple types of leukemia., This work was supported by Project TIN2015-71873-R (Spanish Ministry of Economy and Competitiveness -MINECO- and the European Regional Development Fund -ERDF) and Junta de Andalucı´a (P12–TIC–2082).

Heart disease is currently one of the leading causes of death in developed countries. The electrocardiogram is an important source of information for identifying these conditions, therefore, becomes necessary to seek an advanced system of diagnosis based on these signals. In this paper we used samples of electrocardiograms of MIT-related database with ten types of pathologies and a rate corresponding to normal (healthy patient), which are processed and used for extraction from its two branches of a wide range of features. Next, various techniques have been applied to feature selection based on genetic algorithms, principal component analysis and mutual information. To carry out the task of intelligent classification, 3 different scenarios have been considered. These techniques allow us to achieve greater efficiency in the classification methods used, namely support vector machines (SVM) and decision trees (DT) to perform a comparative analysis between them. Finally, during the development of this contribution, the use of very non-invasive devices (2 channel ECG) was analyzed, we could practically classify them as wearable, which would not need interaction by the user, and whose energy consumption is very small to extend the average life of the user been on it.

Motivation: Multiple sequence alignments (MSAs) are widely used approaches in bioinformatics to carry out other tasks such as structure predictions, biological function analyses or phylogenetic modeling. However, current tools usually provide partially optimal alignments, as each one is focused on specific biological features. Thus, the same set of sequences can produce different alignments, above all when sequences are less similar. Consequently, researchers and biologists do not agree about which is the most suitable way to evaluate MSAs. Recent evaluations tend to use more complex scores including further biological features. Among them, 3D structures are increasingly being used to evaluate alignments. Because structures are more conserved in proteins than sequences, scores with structural information are better suited to evaluate more distant relationships between sequences. Results: The proposed multiobjective algorithm, based on the non-dominated sorting genetic algorithm, aims to jointly optimize three objectives: STRIKE score, non-gaps percentage and totally conserved columns. It was significantly assessed on the BAliBASE benchmark according to the Kruskal–Wallis test (P < 0.01). This algorithm also outperforms other aligners, such as ClustalW, Multiple Sequence Alignment Genetic Algorithm (MSA-GA), PRRP, DIALIGN, Hidden Markov Model Training (HMMT), Pattern-Induced Multi-sequence Alignment (PIMA), MULTIALIGN, Sequence Alignment Genetic Algorithm (SAGA), PILEUP, Rubber Band Technique Genetic Algorithm (RBT-GA) and Vertical Decomposition Genetic Algorithm (VDGA), according to the Wilcoxon signed-rank test (P < 0.05), whereas it shows results not significantly different to 3D-COFFEE (P > 0.05) with the advantage of being able to use less structures. Structural information is included within the objective function to evaluate more accurately the obtained alignments. Availability: The source code is available at http://www.ugr.es/∼fortuno/MOSAStrE/MO-SAStrE.zip. Contact: fortuno@ugr.es Supplementary Information: Supplementary material is available at Bioinformatics online.

In this paper, a novel methodology is presented for optimal placement and selections of knots, for approximating or fitting curves to data, using smoothing splines. It is well-known that the placement of the knots in smoothing spline approximation has an important and considerable effect on the behavior of the final approximation [1] . However, as pointed out in [2] , although spline for approximation is well understood, the knot placement problem has not been dealt with adequately. In the specialized bibliography, several methodologies have been presented for selection and optimization of parameters within B-spline, using techniques based on selecting knots called dominant points , adaptive knots placement, by data selection process, optimal control over the knots, and recently, by using paradigms from computational intelligent, and Bayesian model for automatically determining knot placement in spline modeling. However, a common two-step knot selection strategy, frequently used in the bibliography, is an homogeneous distribution of the knots or equally spaced approach [3] . In order to optimize the placement and numbers of knots required for approximation using smoothing splines, an Evolutionary Computation Paradigms ( ECP ) based on a Multi-Objective Genetic Algorithm has been developed, with the main purpose of avoiding the large number of local minima (in terms of approximation error for different system complexity or number of knots) existing in the problem of knots placement. The accuracy, computationally efficient and robustness of the algorithm presented will be compared by different experimental results, with other approaches presented in the bibliography, showing the main advantages of the proposed methodology.

In the current supplement, we are proud to present ten relevant contributions from the 3rd International Work-Conference on Bioinformatics and Biomedical Engineering (IWBBIO 2015), which was held during April 15–17, 2015 in Granada (Spain) [1, 2]. These contributions have been chosen because of their quality and the importance of their findings. IWBBIO 2015 aimed to follow the same friendly environment as previous editions, allowing researchers, scientists and students to show their latest ideas, discoveries and outcomes. The conference sought to focus on diverse fields to create multidisciplinary researches integrating areas like biomedical engineering, computer science, mathematics, artificial intelligence, bioinformatics, statistics or biomedicine. IWBBIO 2015 also promoted the meeting, cooperation and collaboration of scientists with the presentation and discussion of their latest relevant ideas and work-in-progress. These ideas provided important advances to the scientific community in fields like genomics, next-generation sequencing, drug design and advanced pharmacology, biomedical modelling and e-health, among other. Additionally, IWBBIO 2015 was honored with the presence of three invited plenary speakers: Prof. Alfonso Valencia, Prof. Patrick Aloy and Prof. Xavier Estivill. These plenary lectures strengthened the aim of this conference for the diffusion and the discussion of high quality researches from some of the most recognized scientists in these fields. The IWBBIO 2015 has continued as a two-track conference, increasing the number of sessions to a total of 24 oral and 2 poster sessions. It received more than 210 contributions which were reviewed by at least two referees from our estimated program and steering committees. The conference continues accepting both full and abstract submissions for presentations. However, it still maintained a high rate of full contributions against abstracts. IWBBIO 2015 received more than 200 attendees from diverse European nationalities (Spain, United Kingdom, France, Italy, Poland, etc) but also overseas countries like United Stated, Korea, China or India. Those contributions which were considered more relevant taking into account the evaluation and opinion of reviewers and chairmen were then invited to participate in this supplement for the BioMedical Engineering OnLine journal. In this regard, this supplement is specially based on particular technological implementations and developments oriented to facilitate and improve healthcare and wellness. All the novel researches showed in this supplement have a massively interdisciplinary background, dealing with hot topics in the area of biomedicine and biomedical engineering like e-health, diagnosis or biology systems but also statistics and computer science like cloud computing, big data or mathematical modelling. Thus, the first paper authored by Gimenez et al. [3] describes a mathematical implementation of non-uniform flow models to simulate tilted holes and conical holes in the obstruction of ventricular catheters. Although these simulations have been developed in an ideal scenario, they provide promising results about the consequences of variations in diameters as well as tilt angles of the holes. This novel design improves the disadvantage of having too small holes which appear in other commercially available implementations. The article by Gamberger el al. [4] proposes a novel clustering algorithm which is able to determine small and heterogeneous subpopulations in Alzheimer’s disease (AD) patients. This method, called multilayer clustering, defines each clinical or biological descriptor as a “layer”, building clusters based on the co-existence of properties. This methodology was evaluated with a dataset with 33 properties and 916 patients with significant problems with dementia. Even though the methodology should be validated in other domains, it has shown the successful identification of subpopulations in the AD domain. Abbasi et al. [5] address in their article one traditional but still challenging problem in bioinformatics, the multiple sequence alignment (MSA). In this case, authors introduce a novel heuristic solution based on local search and an optimization based on the number of indels and a substitution score (multiobjective approach). From these bases, several implementations and configurations are proposed and compared including perturbation techniques to avoid suboptimal solutions due to local maximums. Taking into consideration the outcomes, this study has shown a significant improvement in alignments in a reasonable time. Following, the paper by Molina-Recio et al. [6] analyzes the effect of multidisciplinary research when developing mobile applications based on eHealth (also called mHealth). This systematic review highlights the exponential increase of research publications in this area and the importance of interdisciplinary teams involving more health professionals, whose contribution is currently lacking from. Nevertheless, authors of this study also conclude that the impact in the analyzed publications could be not necessarily correlated with the presence of multidisciplinary teams. In the next article, Macdonald et al. [7] presents an adaptive gradient matching approach based on Gaussian processes to estimate parameters in biological pathways, which are habitually expressed as pairs of ordinary differential equations (ODEs). This contribution is mainly based on a mathematical and numerical modelling but with a clearly promising application in the inference of system biology parameters. In fact, according to the provided results, the proposed approach outperforms other similar solutions when estimating parameters in two benchmarks to describe the voltage potential across the cell membrane and the protein signaling transduction pathway, respectively. The paper by Cisar et al. [8] describes BioWes, a complete and well-designed repository to store and to manage experimental descriptors and data. This tool is specifically designed taking into account the importance of “Big Data” and how dealing with it. With this purpose, it includes implementations for desktop application, web interface and web-based interface mobile devices. BioWes has been validated in the context of the AQUAEXCEL international infrastructure project in order to organize and store data from aquaculture experiments. The next contribution by Ortega et al. [9] analyzes different feature selection techniques in multi-resolution analysis (MRA) to classify electroencephalographic (EEG) images for brain-computer interfaces (BCIs). Several feature selection approaches are compared based on evolutionary multi-objective techniques and different structures of classifiers. Authors statistically probe that the proposed feature selection algorithms outperform the baseline MRA in terms of computational costs given the reduction in the number of features, providing similar or better classification performances. Subsequently, Banos et al. [10] present in their publication a system for personalized medicine, healthcare and wellness based on a new digital framework called Mining Minds. The proposed system exploits the advantages of cloud computing storage and high performance computing but with the novelty of incorporating wearable technology and Big Data. This framework addresses an efficient, flexible and robust design which makes it a promising and potential tool for other clinical developments and applications. The article from Larriba et al. [11] proposes a novel baby dinosaur robotic pet, called Pleo, to reduce pain and anxiety in hospitalized children. Simultaneously, this therapeutic pet is wireless connected to assist clinical personal to monitor and understand the child behavior through an Android app. This article describes the whole architecture of Pleo robot in detail but it also shows the successful results obtained from preliminary tests with children in hospital. Finally, Soria-Morillo et al. [12] describe a novel discrete classification algorithm to determine human reactions on TV advertisements from low-cost EEG signals. This algorithm is presented by integrating signal collection/transformation stages with different supervised machine learning approaches like C4.5, ANN or an own-designed discretization procedure. After validating and comparing these classifier’s performances, authors showed their approach clearly outperforms other tools in terms of accuracy and power consumption. As Guest editors, we would like to express our thankfulness to all the authors contributing with their high quality researches to the achievement of this supplement. Also, we are very grateful to expert scientists that have actively collaborated with their recommendations and suggestions to review and improve these contributions. We would like to sincerely acknowledge Profs. Kenneth R. Foster and Fong-Chin Su, Editors-in-Chief of BioMedical Engineering Online, for giving again the opportunity to publish this supplement in this relevant journal. We specially thank to Mr. Omar El Bakry for his excellent and constant support with the publication and edition of this supplement. It has been an honor for us to participate in it. We finally invite authors and readers of this supplement to submit their recent works to future editions of IWBBIO, which will be announced at http://iwbbio.ugr.es.

The majority of clustering methods that have been proposed in the literature are focused on classification problems, and more specifically on pattern recognition problems. In this paper, we provide an enhanced clustering function approximation (ECFA) methodology that is especially suited for function approximation problems, which calculates the error committed in every cluster using the real output of the radial basis function neural network (RBFN), and not just an approximate value of that output, trying to concentrate more clusters in those input regions where the approximation error is bigger, thus attempting to homogenize the contribution to the error of every cluster. ECFA migrates clusters to the zones of the input space where the approximation error is bigger, thus trying to homogenously distribute the total distortion in every cluster, producing a better share-out of clusters for the data input space. We will see that ECFA outperforms other clustering techniques, such as the FCM algorithm or the CFA algorithm not only with respect to the final approximation error but also with respect to the execution time.

Protein-protein interactions (PPIs) play a crucial role in cellular processes. In the present work, a new approach is proposed to construct a PPI predictor training a support vector machine model through a mutual information filter-wrapper parallel feature selection algorithm and an iterative and hierarchical clustering to select a relevance negative training set. By means of a selected suboptimum set of features, the constructed support vector machine model is able to classify PPIs with high accuracy in any positive and negative datasets.

In this paper, a new methodology is developed for defining error and similarity measure indexes, in order to establish a criterion adequate for comparison function approximation using fuzzy data. The proposed similarity measures and error indexes are applied for smoothing with cubic splines, showing a good performance for defining the accuracy of approximation obtained with fuzzy numbers. Examples are given to compare the behavior of the new indexes proposed for different configurations of the smooth cubic splines. A statistical analysis was carried out to verify the homogeneity of the indexes proposed as criteria to determine the correctness or accuracy of such approximation of fuzzy numbers.

Computer-Aided Diagnosis (CAD) represents a relevant instrument to automatically classify between patients with and without Alzheimer's Disease (AD) using several actual imaging techniques. This study analyzes the optimization of volumes of interest (VOIs) to extract three-dimensional (3D) textures from Magnetic Resonance Image (MRI) in order to diagnose AD, Mild Cognitive Impairment converter (MCIc), Mild Cognitive Impairment nonconverter (MCInc) and Normal subjects. A relevant feature of the proposed approach is the use of 3D features instead of traditional two-dimensional (2D) features, by using 3D discrete wavelet transform (3D-DWT) approach for performing feature extraction from T-1 weighted MRI. Due to the high number of coefficients when applying 3D-DWT to each of the VOIs, a feature selection algorithm based on mutual information is used, as is the minimum Redundancy Maximum Relevance (mRMR) algorithm. Region optimization has been performed in order to discover the most relevant regions (VOIs) in the brain with the use of Multi-Objective Genetic Algorithms, being one of the objectives to be optimize the accuracy of the system. The error index of the system is computed by the confusion matrix obtained by the multi-class support vector machine (SVM) classifier. Principal Component Analysis (PCA) is used with the purpose of reducing the number of features to the classifier. The cohort of subjects used in the study consisted of 296 different patients. A first group of 206 patients was used to optimize VOI selection and another group of 90 independent subjects (that did not belong to the first group) was used to test the solutions yielded by the genetic algorithm. The proposed methodology obtains excellent results in multi-class classification achieving accuracies of 94.4% and also extracting significant information on the location of the most relevant points of the brain. This suggests that the proposed method could aid in the research of other neurodegenerative diseases, improving the accuracy of the diagnosis and finding the most relevant regions of the brain associated with them.

The function approximation problem has been tackled many times in the literature by using radial basis function neural networks (RBFNNs). In the design of these neural networks there are several stages where, the most critical stage is the initialization of the centers of each RBF since the rest of the steps to design the RBFNN strongly depend on it. The improved clustering for function approximation (ICFA) algorithm was recently introduced and proved successful for the function approximation problem. In the ICFA algorithm, a fuzzy partition of the input data is performed but, a fuzzy partition can behave inadequately in noise conditions. Possibilistic and mixed approaches, combining fuzzy and possibilistic partitions, were developed in order to improve the performance of a fuzzy partition. In this paper, a study of the influence of replacing the fuzzy partition used in the ICFA algorithm with the possibilistic and the fuzzy-possibilistic partitions will be done. A comparative analysis of each kind of partition will be performed in order to see if the possibilistic approach can improve the performance of the ICFA algorithm both in normal and in noise conditions. The results will show how the employment of a mixed approach combining fuzzy and possibilistic approach can lead to improve the results when designing RBFNNs.

The present work analyses the wireless sensor network protocol (DARP) and the impact of different configuration parameter sets on its performance. Different scenarios have been considered, in order to gain a better understanding of the influence of the configuration on network protocols. The developed statistical analysis is based on the method known as Analysis of Variance (ANOVA), which focuses on the effect of the configuration on the performance of DARP. Three main dependent variables were considered: number of control messages sent during the set-up time, energy consumption and convergence time. A total of 20,413 simulations were carried out to ensure greater robustness in the statistical conclusions. The main goal of this work is to discover the most critical configuration parameters for the protocol, with a view to potential applications in Smart City type scenarios., This work was partially supported by project TIN2015-71873-R (MINECO/FEDER) and TIN2015-67020-P (Spanish “Ministerio de Economía y Competitividad” and European Regional Development Funds (ERDF)).

The main objective of this paper is to prove the great advantage that brings our novel approach to the intelligent control area. A set of various types of intelligent controllers have been designed to control the temperature of a room in a real-time control process in order to compare the obtained results with each other. Through a training board that allows us to control the temperature, all the used algorithms should present their best performances in this control process; therefore, our self-organized and online adaptive fuzzy logic controller (FLC) will be required to present great improvements in the control task and a real high control performance. Simulation results can show clearly that the new approach presented and tested in this work is very efficient. Thus, our adaptive and self-organizing FLC presents the best accuracy compared with the remaining used controllers, and, besides that, it can guarantee an important reduction of the power consumption during the control process.

There are several dementias but Alzheimer's Disease (AD) is leading cause of dementia in the world. In this paper, a new methodology for classification of MR images is proposed, using a large data base (more than one thousand patient). We have two main objectives in this paper: a first one where a classification method is developed to classify MR images as either normal or with the Alzheimer's disease and a second which is the ambitious goal of this study, the identification and classification between normal subjects, MCI patients and AD patients. It is noteworthy that with this last study we could offer a tool to assist the early diagnosis of dementia.

This article focuses on the development of intelligent classifiers in the area of biomedicine, focusing on the problem of diagnosing cardiac diseases based on the electrocardiogram (ECG), or more precisely, on the differentiation of the types of atrial fibrillations. First of all, we will study the ECG, and the treatment of the ECG in order to work with it with this specific pathology. In order to achieve this we will study different ways of elimination, in the best possible way, of any activity that is not caused by the auriculars. We will study and imitate the ECG treatment methodologies and the characteristics extracted from the electrocardiograms that were used by the researchers who obtained the best results in the Physionet Challenge, where the classification of ECG recordings according to the type of atrial fibrillation (AF) that they showed, was realized. We will extract a great amount of characteristics, partly those used by these researchers and additional characteristics that we consider to be important for the distinction previously mentioned. A new method based on evolutionary algorithms will be used to realize a selection of the most relevant characteristics and to obtain a classifier that will be capable of distinguishing the different types of this pathology.

Clustering algorithms have been applied in several disciplines successfully. One of those applications is the initialization of Radial Basis Functions (RBF) centers composing a Neural Network, designed to solve functional approximation problems. The Clustering for Function Approximation (CFA) algorithm was presented as a new clustering technique that provides better results than other clustering algorithms that were traditionally used to initialize RBF centers. Even though CFA improves performance against other clustering algorithms, it has some flaws that can be improved. Within those flaws, it can be mentioned the way the partition of the input data is done, the complex migration process, the algorithm's speed, the existence of some parameters that have to be set in order to obtain good solutions, and the convergence is not guaranteed. In this paper, it is proposed an improved version of this algorithm that solves the problems that its predecessor has using fuzzy logic successfully. In the experiments section, it will be shown how the new algorithm performs better than its predecessor and how important is to make a correct initialization of the RBF centers to obtain small approximation errors.

A new method for extracting valuable process information from input-output data is presented in this paper using a pseudo-gaussian basis function neural network with regression weights. The proposed methodology produces dynamical radial basis function, able to modify the number of neuron within the hidden layer. Other important characteristic of the proposed neural system is that the activation of the hidden neurons is normalized, which, as described in the bibliography, provides better performance than non-normalization. The effectiveness of the method is illustrated through the development of dynamical models for a very well known benchmark, the synthetic time series Mackey-Glass.

In this paper, a temperature control in real time control process was presented using several control algorithms. A quantitative comparison based on the real power consumption and (the precision and the robustness) of these controllers during the same control process and under the same conditions will be done. The proposed Adaptive and Self Organizing Fuzzy policy has been able to prove its superiority against the remaining controllers. The new Adaptive and Self Organizing Fuzzy Logic Controller starts the control with a very limited information about the controlled process (delay and the monotonicity sign) and without any kind of offline pre-training, the adaptive controller acts online to collect the necessary background to adapt their rules consequents and to self organize their membership functions from the real behavior of the controlled process. During 200 minutes and under the same conditions all the performed controllers have been used to control the room temperature, each simulation has been repeated five times with two different sets of set points. These amounts of results was used as a set of sampling for the statistical tool ANOVA (Analysis of Variance) that can prove and illustrate the validity and the extrapolability of the conclusions extracted from several stages of this work.

Multiple sequence alignments (MSAs) have become one of the most studied approaches in bioinformatics to perform other outstanding tasks such as structure prediction, biological function analysis or next-generation sequencing. However, current MSA algorithms do not always provide consistent solutions, since alignments become increasingly difficult when dealing with low similarity sequences. As widely known, these algorithms directly depend on specific features of the sequences, causing relevant influence on the alignment accuracy. Many MSA tools have been recently designed but it is not possible to know in advance which one is the most suitable for a particular set of sequences. In this work, we analyze some of the most used algorithms presented in the bibliography and their dependences on several features. A novel intelligent algorithm based on least square support vector machine is then developed to predict how accurate each alignment could be, depending on its analyzed features. This algorithm is performed with a dataset of 2180 MSAs. The proposed system first estimates the accuracy of possible alignments. The most promising methodologies are then selected in order to align each set of sequences. Since only one selected algorithm is run, the computational time is not excessively increased.

This paper analyzes the performance of some fuzzy implications proposed in the bibliography together with the operators needed for their definition and for the fuzzy inference process. Examining the specialized literature, it is clear that the selection of the best fuzzy implication operator has become one of the main question in the design of a fuzzy system, being occasionally contradictory (at presently there are more than 72 fuzzy implication proposed and investigated). An approach to the problem from a different perspective is given. The question is to determine whether the selection of the fuzzy implication operator is more important with respect to the behaviour of the fuzzy system than the operators (mainly T-norm, T-conorm and defuzzification method) involved in the definition of the implication function and in the rest of the inference process. The relevance and relative importance of the operators involved in the fuzzy inference process are investigated by using a powerful statistical tool, the ANalysis Of the VAriance (ANOVA) [Box et al., Statistics for experiments: an introduction to design, data analysis and model building, Wiley, New York, 1978; Montgomery, Design and Analysis of Experiments, Wiley, New York, 1984].

In this paper we present a new approach for the problem of approximating a function from a training set of I/O points using fuzzy logic and fuzzy systems. Such approach, as we will see, will provide us a number of advantages comparing to other more-limited systems. Among these advantages, we may highlight the considerable reduction in the number of rules needed to model the underlined function of this set of data and, from other point of view, the possibility of bringing interpretation to the rules of the system obtained, using the Taylor Series concept. This work is reinforced by an algorithm able to obtain the pseudo-optimal polynomial consequents of the rules. Finally the performance of our approach and that of the associated algorithm are shown through a significant example.