A Java Simulator for Membrane Computing

Membrane Computing is a recent area of Natural Computing, a topic where much work has been done but still much remains to be done. There are some applica tions which have been developed in imperative languages, like C++, or in declaratives languages, as Prolog, working in the framework of P systems. In this paper, a software tool (called SimCM, from Spanish Simulador de Computaci´on con Membranas) for handling P systems is presented. The program can simulate basic transition P Systems where dissolution of membranes and priority rules are allowed. The software applica tion is carried out in an imperative and object-oriented language – Java. We choose Java because it is a scalable and distributed language. Working with Java is the first step to cross the border between simulations and a distributed implementation able to capture the parallelism existing in the membrane computing area. This tool is a friendly application which allows us to follow the evolution of a P system easily and in a visual way. The program can be used to move the P system theory closer to the biologist and all the people who wants to learn and understand how this model works

A Java Simulator for Basic Transition P Systems

In this paper, a software tool (called SimCM, from Spanish Sim- ulador de Computaci¶on con Membranas) for handling P systems is presented. The program can simulate basic transition P Systems where dissolution of membranes and priority rules are allowed. This is a ¯rst step to cross the border between simulations and distributed implementations that capture the parallelism existing in this model

Pairwise gene GO-based measures for biclustering of high-dimensional expression data

Background: Biclustering algorithms search for groups of genes that share the same behavior under a subset of samples in gene expression data. Nowadays, the biological knowledge available in public repositories can be used to drive these algorithms to find biclusters composed of groups of genes functionally coherent. On the other hand, a distance among genes can be defined according to their information stored in Gene Ontology (GO). Gene pairwise GO semantic similarity measures report a value for each pair of genes which establishes their functional similarity. A scatter search-based algorithm that optimizes a merit function that integrates GO information is studied in this paper. This merit function uses a term that addresses the information through a GO measure. Results: The effect of two possible different gene pairwise GO measures on the performance of the algorithm is analyzed. Firstly, three well known yeast datasets with approximately one thousand of genes are studied. Secondly, a group of human datasets related to clinical data of cancer is also explored by the algorithm. Most of these data are high-dimensional datasets composed of a huge number of genes. The resultant biclusters reveal groups of genes linked by a same functionality when the search procedure is driven by one of the proposed GO measures. Furthermore, a qualitative biological study of a group of biclusters show their relevance from a cancer disease perspective. Conclusions: It can be concluded that the integration of biological information improves the performance of the biclustering process. The two different GO measures studied show an improvement in the results obtained for the yeast dataset. However, if datasets are composed of a huge number of genes, only one of them really improves the algorithm performance. This second case constitutes a clear option to explore interesting datasets from a clinical point of view.Ministerio de Economía y Competitividad TIN2014-55894-C2-

Databases Reduction Simultaneously by Ordered Projection

In this paper, a new algorithm Database Reduction Simulta neously by Ordered Projections (RESOP) is introduced. This algorithm reduces databases in two directions: editing examples and feature se lection simultaneously. Ordered projections techniques have been used to design RESOP taking advantage of symmetrical ideas for two dif ferent task. Experimental results have been made with UCI Repository databases and the performance for the latter application of classification techniques has been satisfactor

Biclustering of Gene Expression Data Based on SimUI Semantic Similarity Measure

Biclustering is an unsupervised machine learning technique that simultaneously clusters genes and conditions in gene expression data. Gene Ontology (GO) is usually used in this context to validate the biological relevance of the results. However, although the integration of biological information from different sources is one of the research directions in Bioinformatics, GO is not used in biclustering as an input data. A scatter search-based algorithm that integrates GO information during the biclustering search process is presented in this paper. SimUI is a GO semantic similarity measure that defines a distance between two genes. The algorithm optimizes a fitness function that uses SimUI to integrate the biological information stored in GO. Experimental results analyze the effect of integration of the biological information through this measure. A SimUI fitness function configuration is experimentally studied in a scatter search-based biclustering algorithmMinisterio de Ciencia e Innovación TIN2011-28956-C02-02Ministerio de Ciencia e Innovación TIN2014-55894-C2-RJunta de Andalucía P12-TIC-1728Universidad Pablo de Olavide APPB81309

A Measure for Data Set Editing by Ordered Projections

In this paper we study a measure, named weakness of an example, which allows us to establish the importance of an example to find representative patterns for the data set editing problem. Our ap proach consists in reducing the database size without losing information, using algorithm patterns by ordered projections. The idea is to relax the reduction factor with a new parameter, λ, removing all examples of the database whose weakness verify a condition over this λ. We study how to establish this new parameter. Our experiments have been carried out using all databases from UCI-Repository and they show that is possible a size reduction in complex databases without notoriously increase of the error rate

Building Transcriptional Association Networks in Cytoscape with RegNetC

The Regression Network plugin for Cytoscape (RegNetC) implements the RegNet algorithm for the inference of transcriptional association network from gene expression profiles. This algorithm is a model tree-based method to detect the relationship between each gene and the remaining genes simultaneously instead of analyzing individually each pair of genes as correlation-based methods do. Model trees are a very useful technique to estimate the gene expression value by regression models and favours localized similarities over more global similarity, which is one of the major drawbacks of correlation-based methods. Here, we present an integrated software suite, named RegNetC, as a Cytoscape plugin that can operate on its own as well. RegNetC facilitates, according to user-defined parameters, the resulted transcriptional gene association network in .sif format for visualization, analysis and interoperates with other Cytoscape plugins, which can be exported for publication figures. In addition to the network, the RegNetC plugin also provides the quantitative relationships between genes expression values of those genes involved in the inferred network, i.e., those defined by the regression modelsMinisterio de Ciencia y Tecnología TIN2007-68084-C00Junta de Andalucía P11-TIC-752

Deep Learning Techniques to Improve the Performance of Olive Oil Classification

The olive oil assessment involves the use of a standardized sensory analysis according to the “panel test” method. However, there is an important interest to design novel strategies based on the use of Gas Chromatography (GC) coupled to mass spectrometry (MS), or ion mobility spectrometry (IMS) together with a chemometric data treatment for olive oil classification. It is an essential task in an attempt to get the most robust model over time and, both to avoid fraud in the price and to know whether it is suitable for consumption or not. The aim of this paper is to combine chemical techniques and Deep Learning approaches to automatically classify olive oil samples from two different harvests in their three corresponding classes: extra virgin olive oil (EVOO), virgin olive oil (VOO), and lampante olive oil (LOO). Our Deep Learning model is built with 701 samples, which were obtained from two olive oil campaigns (2014–2015 and 2015–2016). The data from the two harvests are built from the selection of specific olive oil markers from the whole spectral fingerprint obtained with GC-IMS method. In order to obtain the best results we have configured the parameters of our model according to the nature of the data. The results obtained show that a deep learning approach applied to data obtained from chemical instrumental techniques is a good method when classifying oil samples in their corresponding categories, with higher success rates than those obtained in previous works.Ministerio de Economía y Competitividad TIN2017-88209-C2-2-

Inferencia de Redes de Asociación de Genes Guiada por Similitud Semántica

En este trabajo se propone el uso de conocimiento a priori como heurística en métodos de inferencia de redes de genes a partir de datos de expresión obtenidos con tecnología de Microarray. Utilizamos Gene Ontology [15] como fuente de conocimiento a priori. Este repositorio se nutre de la información de anotaciones de relaciones en el material genético basadas en evidencias científicas. En este trabajo se propone el uso de medidas de similitud semántica, de manera más concreta la medida SimGIC en un método de inferencia basado en regresión. La propuesta se compara frente al mismo método sin integración de información y frente a otros métodos clásicos obteniendo mejoras y resultados comparables en otros casos