A Predictive Model for Guillain-Barré Syndrome Based on Single Learning Algorithms

Background. Guillain-Barré Syndrome (GBS) is a potentially fatal autoimmune neurological disorder. The severity varies among the four main subtypes, named as Acute Inflammatory Demyelinating Polyneuropathy (AIDP), Acute Motor Axonal Neuropathy (AMAN), Acute Motor Sensory Axonal Neuropathy (AMSAN), and Miller-Fisher Syndrome (MF). A proper subtype identification may help to promptly carry out adequate treatment in patients. Method. We perform experiments with 15 single classifiers in two scenarios: four subtypes’ classification and One versus All (OvA) classification. We used a dataset with the 16 relevant features identified in a previous phase. Performance evaluation is made by 10-fold cross validation (10-FCV). Typical classification performance measures are used. A statistical test is conducted in order to identify the top five classifiers for each case. Results. In four GBS subtypes’ classification, half of the classifiers investigated in this study obtained an average accuracy above 0.90. In OvA classification, the two subtypes with the largest number of instances resulted in the best classification results. Conclusions. This study represents a comprehensive effort on creating a predictive model for Guillain-Barré Syndrome subtypes. Also, the analysis performed in this work provides insight about the best single classifiers for each classification case

MONIL Language, an Alternative for Data Integration El Lenguaje MONIL, una Alternativa para la Integración de Datos

Abstract Data integration is a process of retrieving, merging and storing of data originated in heterogeneous sources of data. The main problem facing the data integration is the structural and semantic heterogeneity of participating data. A concern of research communities in computer sciences is the development of semi-automatic tools to assist the user in an effective way in the data integration processes. This paper introduces a programming language called MONIL, as an alternative to integrate data by means of design, storage and program execution. MONIL is based on the use of meta-data, conversion functions, a meta-model of integration and a scheme of integration suggestions. MONIL offers to the user a dedicated work environment with built-in semi-automatic tools supporting the integration process in three stages. Keywords: data integration, integration language, databases, metadata. Resumen La integración de datos es el proceso de extracción, mezcla y almacenamiento de datos provenientes de fuentes de datos heterogéneas. El problema principal que enfrenta la integración de datos es la heterogeneidad estructural y semántica de los datos que participan. Una preocupación en las comunidades de investigación de las ciencias computacionales, es el desarrollo de herramientas semiautomáticas que asistan a los usuarios de forma efectiva en los procesos de integración de datos. Este artículo presenta un lenguaje de programación llamado MONIL, como una alternativa para integrar datos mediante el diseño, almacenamiento y ejecución de programas. MONIL está basado en el uso de metadatos, funciones de conversión, un metamodelo de integración y un esquema de sugerencias de integración. MONIL ofrece al usuario un ambiente de trabajo dedicado con herramientas semiautomáticas integradas y que soportan un proceso de integración en tres etapas. Palabras claves: integración de datos, lenguaje de integración, bases de datos, bodegas de datos, metadatos

Knowledge Discovery in Spectral Data by Means of Complex Networks

In the last decade, complex networks have widely been applied to the study of many natural and man-made systems, and to the extraction of meaningful information from the interaction structures created by genes and proteins. Nevertheless, less attention has been devoted to metabonomics, due to the lack of a natural network representation of spectral data. Here we define a technique for reconstructing networks from spectral data sets, where nodes represent spectral bins, and pairs of them are connected when their intensities follow a pattern associated with a disease. The structural analysis of the resulting network can then be used to feed standard data-mining algorithms, for instance for the classification of new (unlabeled) subjects. Furthermore, we show how the structure of the network is resilient to the presence of external additive noise, and how it can be used to extract relevant knowledge about the development of the disease

Lo glocal y el turismo. Nuevos paradigmas de interpretación.

El estudio del turismo se realiza desde múltiples escalas y enfoques, este libro aborda muchos temas que es necesario discutir desde diversas perspectivas; es el caso de la reflexión sobre la propia disciplina y sus conceptos, así como los asuntos específicos referidos al impacto territorial, los tipos de turismo, las cuestiones ambientales, el tema de la pobreza, la competitividad, las políticas públicas, el papel de las universidades, las áreas naturales protegidas, la sustentabilidad, la cultura, el desarrollo, la seguridad, todos temas centrales documentados y expuestos con originalidad y dominio del asunto. Lo multiescalar es básico para la comprensión del sistema turístico, sistema formado de procesos globales, regionales y locales. El eje de discusión del libro es lo glocal, esa interacción entre lo nacional y local con lo global

GRSA Enhanced for Protein Folding Problem in the Case of Peptides

Protein folding problem (PFP) consists of determining the functional three-dimensional structure of a target protein. PFP is an optimization problem where the objective is to find the structure with the lowest Gibbs free energy. It is significant to solve PFP for use in medical and pharmaceutical applications. Hybrid simulated annealing algorithms (HSA) use a kind of simulated annealing or Monte Carlo method, and they are among the most efficient for PFP. The instances of PFP can be classified as follows: (a) Proteins with a large number of amino acids and (b) peptides with a small number of amino acids. Several HSA have been positively applied for the first case, where I-Tasser has been one of the most successful in the CASP competition. PEP-FOLD3 and golden ratio simulated annealing (GRSA) are also two of these algorithms successfully applied to peptides. This paper presents an enhanced golden simulated annealing (GRSA2) where soft perturbations (collision operators), named “on-wall ineffective collision” and “intermolecular ineffective collision”, are applied to generate new solutions in the metropolis cycle. GRSA2 is tested with a dataset for peptides previously proposed, and a comparison with PEP-FOLD3 and I-Tasser is presented. According to the experimentation, GRSA2 has an equivalent performance to those algorithms

Convolutional Neural Network–Component Transformation (CNN–CT) for Confirmed COVID-19 Cases

The COVID-19 disease constitutes a global health contingency. This disease has left millions people infected, and its spread has dramatically increased. This study proposes a new method based on a Convolutional Neural Network (CNN) and temporal Component Transformation (CT) called CNN–CT. This method is applied to confirmed cases of COVID-19 in the United States, Mexico, Brazil, and Colombia. The CT changes daily predictions and observations to weekly components and vice versa. In addition, CNN–CT adjusts the predictions made by CNN using AutoRegressive Integrated Moving Average (ARIMA) and Exponential Smoothing (ES) methods. This combination of strategies provides better predictions than most of the individual methods by themselves. In this paper, we present the mathematical formulation for this strategy. Our experiments encompass the fine-tuning of the parameters of the algorithms. We compared the best hybrid methods obtained with CNN–CT versus the individual CNN, Long Short-Term Memory (LSTM), ARIMA, and ES methods. Our results show that our hybrid method surpasses the performance of LSTM, and that it consistently achieves competitive results in terms of the MAPE metric, as opposed to the individual CNN and ARIMA methods, whose performance varies largely for different scenarios

Predictive ability of machine learning methods for massive crop yield prediction

An important issue for agricultural planning purposes is the accurate yield estimation for the numerous crops involved in the planning. Machine learning (ML) is an essential approach for achieving practical and effective solutions for this problem. Many comparisons of ML methods for yield prediction have been made, seeking for the most accurate technique. Generally, the number of evaluated crops and techniques is too low and does not provide enough information for agricultural planning purposes. This paper compares the predictive accuracy of ML and linear regression techniques for crop yield prediction in ten crop datasets. Multiple linear regression, M5-Prime regression trees, perceptron multilayer neural networks, support vector regression and k-nearest neighbor methods were ranked. Four accuracy metrics were used to validate the models: the root mean square error (RMS), root relative square error (RRSE), normalized mean absolute error (MAE), and correlation factor (R). Real data of an irrigation zone of Mexico were used for building the models. Models were tested with samples of two consecutive years. The results show that M5- Prime and k-nearest neighbor techniques obtain the lowest average RMSE errors (5.14 and 4.91), the lowest RRSE errors (79.46% and 79.78%), the lowest average MAE errors (18.12% and 19.42%), and the highest average correlation factors (0.41 and 0.42). Since M5-Prime achieves the largest number of crop yield models with the lowest errors, it is a very suitable tool for massive crop yield prediction in agricultural plannin