Obtendo información útil para a mellora dunha materia a partir dos resultados dos exames de resposta múltiple

[Resumo] Os procesos de avaliación, deben aplicarse ós docentes e mesmo á materia en si, non só ós alumnos. Con esta finalidade formúlase unha análise dos resultados acadados polo alumnado durante a proba de avaliación empregada na materia de Marcos de Desenvolvemento (Grao en Enxeñaría Informática – Facultade de Informática). O exame é de resposta múltiple (4 opcións por pregunta, só unha válida e restando puntos as respostas erróneas). Os exames analízanse en dúas ramas: por unha banda, estúdanse as taxas de acerto/fallo/en branco de cada unha das preguntas; por outra, a porcentaxe de opcións (a,b,c,d, branco) en cada pregunta. Este sinxelo estudo, automatizado mediante o emprego dunha folla de cálculo, permite, non obstante, obter interesantes conclusións: • Detecta ambigüidades ou erros na formulación das preguntas que, polo xeral, se derivan nunha elevada porcentaxe de respostas en branco. • Detecta lagoas de coñecemento nalgunha das áreas da materia, que orixinan preguntas cunha elevada taxa de erros. Cada pregunta está asociada a un bloque teórico, polo que se podes establecer en qué aspectos os alumnos presentan máis ou menos coñecementos. Ambos aspectos poden ser empregados para detectar erros na formulación da materia e/ou exame e facer posible así a definición de plans de mellora de cara ós vindeiros cursos académicos

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

[Resumen] 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.[Abstract] 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, 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

Net-Net Auto Machine Learning (AutoML) Prediction of Complex Ecosystems

Biological Ecosystem Networks (BENs) are webs of biological species (nodes) establishing trophic relationships (links). Experimental confirmation of all possible links is difficult and generates a huge volume of information. Consequently, computational prediction becomes an important goal. Artificial Neural Networks (ANNs) are Machine Learning (ML) algorithms that may be used to predict BENs, using as input Shannon entropy information measures (Sh(k)) of known ecosystems to train them. However, it is difficult to select a priori which ANN topology will have a higher accuracy. Interestingly, Auto Machine Learning (AutoML) methods focus on the automatic selection of the more efficient ML algorithms for specific problems. In this work, a preliminary study of a new approach to AutoML selection of ANNs is proposed for the prediction of BENs. We call it the Net-Net AutoML approach, because it uses for the first time Shk values of both networks involving BENs (networks to be predicted) and ANN topologies (networks to be tested). Twelve types of classifiers have been tested for the Net-Net model including linear, Bayesian, trees-based methods, multilayer perceptrons and deep neuronal networks. The best Net-Net AutoML model for 338,050 outputs of 10 ANN topologies for links of 69 BENs was obtained with a deep fully connected neuronal network, characterized by a test accuracy of 0.866 and a test AUROC of 0.935. This work paves the way for the application of Net-Net AutoML to other systems or ML algorithms.The authors acknowledge Basque Government (Eusko Jaurlaritza) grant (IT1045-16) - 2016-2021 for consolidated research groups. This work was supported by the "Collaborative Project in Genomic Data Integration (CICLOGEN)" PI17/01826 funded by the Carlos III Health Institute, as part of the Spanish National plan for Scientific and Technical Research and Innovation 2013-2016 and the European Regional Development Funds (FEDER). This project was also supported by the General Directorate of Culture, Education and University Management of Xunta de Galicia ED431D 2017/16 and "Drug Discovery Galician Network" Ref. ED431G/01 and the "Galician Network for Colorectal Cancer Research" (Ref. ED431D 2017/23), and finally by the Spanish Ministry of Economy and Competitiveness for its support through the funding of the unique installation BIOCAI (UNLC08-1E-002, UNLC13-13-3503) and the European Regional Development Funds (FEDER) by the European Union. CR Munteanu acknowledges the support of NVIDIA Corporation with the donation of the Titan Xp GPU used for this research

Sobre la ocurrencia del cretáceo superior marino en Coihaique , Provincia de Aisén

Using the CCSD­(T) model, we evaluated the intermolecular potential energy surfaces of the He–, Ne–, and Ar–phosgene complexes. We considered a representative number of intermolecular geometries for which we calculated the corresponding interaction energies with the augmented (He complex) and double augmented (Ne and Ar complexes) correlation-consistent polarized valence triple-ζ basis sets extended with a set of 3s3p2d1f1g midbond functions. These basis sets were selected after systematic basis set studies carried out at geometries close to those of the surface minima. The He–, Ne–, and Ar–phosgene surfaces were found to have absolute minima of −72.1, −140.4, and −326.6 cm^–1 at distances between the rare-gas atom and the phosgene center of mass of 3.184, 3.254, and 3.516 Å, respectively. The potentials were further used in the evaluation of rovibrational states and the rotational constants of the complexes, providing valuable results for future experimental investigations. Comparing our results to those previously available for other phosgene complexes, we suggest that the results for Cl₂–phosgene should be revised

Automatic Seizure Detection Based on Star Graph Topological Indices

[Abstract] The recognition of seizures is very important for the diagnosis of patients with epilepsy. The seizure is a process of rhythmic discharge in brain and occurs rarely and unpredictably. This behavior generates a need of an automatic detection of seizures by using the signals of long-term electroencephalography (EEG) recordings. Due to the non-stationary character of EEG signals, the conventional methods of frequency analysis are not the best alternative to obtain good results in diagnostic purpose. The present work proposes a method of EEG signal analysis based on star graph topological indices (SGTIs) for the first time. The signal information, such as amplitude and time occurrence, is codified into invariant SGTIs which are the basis for the classification models that can discriminate the epileptic EEG records from the non-epileptic ones. The method with SGTIs and the simplest linear discriminant methods provide similar results to those previously published, which are based on the time-frequency analysis and artificial neural networks. Thus, this work proposes a simpler and faster alternative for automatic detection of seizures from the EEG recordings.Xunta de Galicia; 2007/127Xunta de Galicia; 2007/144Instituto de Salud Carlos III; PIO52048Instituto de Salud Carlos III; RD07/0067/0005Ministerio de Ciencia e Innovación; TIN2009—07707

Evolutionary Computation and QSAR Research

[Abstract] The successful high throughput screening of molecule libraries for a specific biological property is one of the main improvements in drug discovery. The virtual molecular filtering and screening relies greatly on quantitative structure-activity relationship (QSAR) analysis, a mathematical model that correlates the activity of a molecule with molecular descriptors. QSAR models have the potential to reduce the costly failure of drug candidates in advanced (clinical) stages by filtering combinatorial libraries, eliminating candidates with a predicted toxic effect and poor pharmacokinetic profiles, and reducing the number of experiments. To obtain a predictive and reliable QSAR model, scientists use methods from various fields such as molecular modeling, pattern recognition, machine learning or artificial intelligence. QSAR modeling relies on three main steps: molecular structure codification into molecular descriptors, selection of relevant variables in the context of the analyzed activity, and search of the optimal mathematical model that correlates the molecular descriptors with a specific activity. Since a variety of techniques from statistics and artificial intelligence can aid variable selection and model building steps, this review focuses on the evolutionary computation methods supporting these tasks. Thus, this review explains the basic of the genetic algorithms and genetic programming as evolutionary computation approaches, the selection methods for high-dimensional data in QSAR, the methods to build QSAR models, the current evolutionary feature selection methods and applications in QSAR and the future trend on the joint or multi-task feature selection methods.Instituto de Salud Carlos III, PIO52048Instituto de Salud Carlos III, RD07/0067/0005Ministerio de Industria, Comercio y Turismo; TSI-020110-2009-53)Galicia. Consellería de Economía e Industria; 10SIN105004P

Net-Net AutoML Selection of Artificial Neural Network Topology for Brain Connectome Prediction

[Abstract] Brain Connectome Networks (BCNs) are defined by brain cortex regions (nodes) interacting with others by electrophysiological co-activation (edges). The experimental prediction of new interactions in BCNs represents a difficult task due to the large number of edges and the complex connectivity patterns. Fortunately, we can use another special type of networks to achieve this goal—Artificial Neural Networks (ANNs). Thus, ANNs could use node descriptors such as Shannon Entropies (Sh) to predict node connectivity for large datasets including complex systems such as BCN. However, the training of a high number of ANNs for BCNs is a time-consuming task. In this work, we propose the use of a method to automatically determine which ANN topology is more efficient for the BCN prediction. Since a network (ANN) is used to predict the connectivity in another network (BCN), this method was entitled Net-Net AutoML. The algorithm uses Sh descriptors for pairs of nodes in BCNs and for ANN predictors of BCNs. Therefore, it is able to predict the efficiency of new ANN topologies to predict BCNs. The current study used a set of 500,470 examples from 10 different ANNs to predict node connectivity in BCNs and 20 features. After testing five Machine Learning classifiers, the best classification model to predict the ability of an ANN to evaluate node interactions in BCNs was provided by Random Forest (mean test AUROC of 0.9991 ± 0.0001, 10-fold cross-validation). Net-Net AutoML algorithms based on entropy descriptors may become a useful tool in the design of automatic expert systems to select ANN topologies for complex biological systems. The scripts and dataset for this project are available in an open GitHub repository.Instituto de Salud Carlos III; PI17/01826Gobierno Vasco; IT1045-16Xunta de Galicia; ED431D 2017/16Xunta de Galicia; ED431C 2018/49Ministerio de Economía y Empresa; BIA2017-86738-RMinisterio de Economía y Empresa; UNLC08-1E-002Ministerio de Economía y Empresa; UNLC13-13-350

Biomedical data integration in computational drug design and bioinformatics

[Abstract In recent years, in the post genomic era, more and more data is being generated by biological high throughput technologies, such as proteomics and transcriptomics. This omics data can be very useful, but the real challenge is to analyze all this data, as a whole, after integrating it. Biomedical data integration enables making queries to different, heterogeneous and distributed biomedical data sources. Data integration solutions can be very useful not only in the context of drug design, but also in biomedical information retrieval, clinical diagnosis, system biology, etc. In this review, we analyze the most common approaches to biomedical data integration, such as federated databases, data warehousing, multi-agent systems and semantic technology, as well as the solutions developed using these approaches in the past few years.Red Gallega de Investigación sobre Cáncer Colorrectal; Ref. 2009/58Programa Iberoamericano de Ciencia y Tecnología para el Desarrollo; 209RT- 0366Instituto de Salud Carlos III; PIO52048Instituto de Salud Carlos III; RD07/0067/0005Ministerio de Industria, Turismo y Comercio; TSI-020110-2009-