Dataset of fundus images for the diagnosis of ocular toxoplasmosis.

Toxoplasmosis chorioretinitis is commonly diagnosed by an ophthalmologist through the evaluation of the fundus images of a patient. Early detection of these lesions may help to prevent blindness. In this article we present a data set of fundus images labeled into three categories: healthy eye, inactive and active chorioretinitis. The dataset was developed by three ophthalmologists with expertise in toxoplasmosis detection using fundus images. The dataset will be of great use to researchers working on ophthalmic image analysis using artificial intelligence techniques for the automatic detection of toxoplasmosis chorioretinitis.

Correlation Between Quality Evaluation Metrics and Teeth Detection Results in Panoramic X-Rays Using Deep Learning.

Panoramic images are one of the most requested exams by dentists for allowing the visualization of the entire mouth. Interpreting X-ray images is a time-consuming task in which misdiagnoses can occur due to the inexperience or fatigue of professionals. In this work, we applied different image enhancement techniques as a pre-processing step to determine which image features correlate with improvements in teeth detection in panoramic images using deep learning architectures. We contrasted the performance of five object-detection architectures using 300 panoramic images of a public dataset. We evaluated the enhancement in the pre-processing step and the detection performance. Quality and detection metrics were considered, and the cross-correlation between them was computed for every object-detection method contemplated. We observe the dependence of the detection performance with some image enhancement techniques, especially those that introduce less noise and preserve the global contrast of the image.

Automatic Diagnosis of Diabetic Retinopathy from Fundus Images Using Neuro-Evolutionary Algorithms.

Due to the presence of high glucose levels, diabetes mellitus (DM) is a widespread disease that can damage blood vessels in the retina and lead to loss of the visual system. To combat this disease, called Diabetic Retinopathy (DR), retinography, using images of the fundus of the retina, is the most used method for the diagnosis of Diabetic Retinopathy. The Deep Learning (DL) area achieved high performance for the classification of retinal images and even achieved almost the same human performance in diagnostic tasks. However, the performance of DL architectures is highly dependent on the optimal configuration of the hyperparameters. In this article, we propose the use of Neuroevolutionary Algorithms to optimize the hyperparameters corresponding to the DL model for the diagnosis of DR. The results obtained prove that the proposed method outperforms the results obtained by the classical approach.

Distribution level electric current consumption and meteorological data set of the east region of Paraguay.

This paper presents a data set with information on meteorological data and electricity consumption in the department of Alto Paraná, Paraguay. The meteorological data were registered every three hours at the Aeropuerto Guarani, Department of Alto Paraná, which belongs to the Dirección Nacional de Aeronáutica Civil of Paraguay. The final data consists of a total of 22.445 records of temperature, relative humidity, wind speed and atmospheric pressure. On the other hand, the electrical energy consumption data set contains a total of 1.848.947 records, all of them coming from the one hundred and fifteen feeders located throughout the Alto Paraná region of Paraguay. Electrical energy consumption data was provided by Administración Nacional de Electricidad (ANDE). The analysis of this data can yield insights regarding the energy consumption in the area.

A Trust-Based Methodology to Evaluate Deep Learning Models for Automatic Diagnosis of Ocular Toxoplasmosis from Fundus Images.

In the automatic diagnosis of ocular toxoplasmosis (OT), Deep Learning (DL) has arisen as a powerful and promising approach for diagnosis. However, despite the good performance of the models, decision rules should be interpretable to elicit trust from the medical community. Therefore, the development of an evaluation methodology to assess DL models based on interpretability methods is a challenging task that is necessary to extend the use of AI among clinicians. In this work, we propose a novel methodology to quantify the similarity between the decision rules used by a DL model and an ophthalmologist, based on the assumption that doctors are more likely to trust a prediction that was based on decision rules they can understand. Given an eye fundus image with OT, the proposed methodology compares the segmentation mask of OT lesions labeled by an ophthalmologist with the attribution matrix produced by interpretability methods. Furthermore, an open dataset that includes the eye fundus images and the segmentation masks is shared with the community. The proposal was tested on three different DL architectures. The results suggest that complex models tend to perform worse in terms of likelihood to be trusted while achieving better results in sensitivity and specificity.

Dataset from fundus images for the study of diabetic retinopathy.

This article presents a database containing 757 color fundus images acquired at the Department of Ophthalmology of the Hospital de Clínicas, Facultad de Ciencias Médicas (FCM), Universidad Nacional de Asunción (UNA), Paraguay. Firstly, the retinal images were acquired with a clinical procedure presented in this paper. The acquisition of the retinographies was made through the Visucam 500 camera of the Zeiss brand. Next, two expert ophthalmologists have classified the dataset. These data can help physicians and researchers in the detection of cases of Non-Proliferative Diabetic Retinopathy (NPDR) and Proliferative Diabetic Retinopathy (PDR), in their different stages. The dataset generated will be useful for ophthalmologists and researchers to work on automatic detection algorithms for Diabetic Retinopathy (DR).

Automatic Diagnosis of Ocular Toxoplasmosis from Fundus Images with Residual Neural Networks.

Ocular toxoplasmosis (OT) is commonly diagnosed through the analysis of fundus images of the eye by a specialist. Despite Deep Learning being widely used to process and recognize pathologies in medical images, the diagnosis of ocular toxoplasmosis(OT) has not yet received much attention. A predictive computational model is a valuable time-saving option if used as a support tool for the diagnosis of OT. It could also help diagnose atypical cases, being particularly useful for ophthalmologists who have less experience. In this work, we propose the use of a deep learning model to perform automatic diagnosis of ocular toxoplasmosis from images of the eye fundus. A pretrained residual neural network is fine-tuned on a dataset of samples collected at the medical center of Hospital de Clínicas in Asunción, Paraguay. With sensitivity and specificity rates equal to 94% and 93%,respectively, the results show that the proposed model is highly promising. In order to replicate the results and advance further in this area of research, an open data set of images of the eye fundus labeled by ophthalmologists is made available.

Panoramic Dental Radiography Image Enhancement Using Multiscale Mathematical Morphology.

Panoramic dental radiography is one of the most used images of the different dental specialties. This radiography provides information about the anatomical structures of the teeth. The correct evaluation of these radiographs is associated with a good quality of the image obtained. In this study, 598 patients were consecutively selected to undergo dental panoramic radiography at the Department of Radiology of the Faculty of Dentistry, Universidad Nacional de Asunción. Contrast enhancement techniques are used to enhance the visual quality of panoramic dental radiographs. Specifically, this article presents a new algorithm for contrast, detail and edge enhancement of panoramic dental radiographs. The proposed algorithm is called Multi-Scale Top-Hat transform powered by Geodesic Reconstruction for panoramic dental radiography enhancement (MSTHGR). This algorithm is based on multi-scale mathematical morphology techniques. The proposal extracts multiple features of brightness and darkness, through the reconstruction of the marker (obtained by the Top-Hat transformation by reconstruction) starting from the mask (obtained by the classic Top-Hat transformation). The maximum characteristics of brightness and darkness are added to the dental panoramic radiography. In this way, the contrast, details and edges of the panoramic radiographs of teeth are improved. For the tests, MSTHGR was compared with the following algorithms: Geodesic Reconstruction Multiscale Morphology Contrast Enhancement (GRMMCE), Histogram Equalization (HE), Brightness Preserving Bi-Histogram Equalization (BBHE), Dual Sub-Image Histogram Equalization (DSIHE), Minimum Mean Brightness Error Bi-Histogram Equalization (MMBEBHE), Quadri-Histogram Equalization with Limited Contrast (QHELC), Contrast-Limited Adaptive Histogram Equalization (CLAHE) and Gamma Correction (GC). Experimentally, the numerical results show that the MSTHGR obtained the best results with respect to the Contrast Improvement Ratio (CIR), Entropy (E) and Spatial Frequency (SF) metrics. This indicates that the algorithm performs better local enhancements on panoramic radiographs, improving their details and edges.

Comparative Transcriptome Profile between Iberian Pig Varieties Provides New Insights into Their Distinct Fat Deposition and Fatty Acids Content.

The high deposition of intramuscular fat and the content of oleic fatty acid are characteristic of the Iberian pig. These two parameters present great variability and are differentiated amongst the varieties that make up the Iberian pig population. Although previous studies generated evidence for causal genes and polymorphisms associated to the adipogenic potential of the Iberian pig, there is little information about how genetic expression influences this trait's variability. The aim of this study was to analyses the expression profile between two varieties of Iberian pig (Torbiscal and Retinto) and their reciprocal crosses differentiated in their intramuscular fat (IMF) content and fatty acid (FA) composition in the Longissimus thoracis muscle using an RNA-seq approach. Our results corroborate that the Retinto variety is the fattiest amongst all studied varieties as its upregulated genes, such as FABP3 and FABP5, SLC27A1 and VEGFA among others, contribute to increasing adiposity. In its turn, Torbiscal pigs showed an upregulation of genes associated with the inhibition of fat deposition such as ADIPOQ and CPT1A. Further genetic variation analysis in these Iberian varieties showed relevant associations for SNP located within the differentially expressed genes with IMF and FA content. Thus, the differences found in the genetic architecture and the muscle transcriptome of these Iberian varieties might explain the variability in their fat content and composition and hence, their meat quality.

Additive and Dominance Genomic Analysis for Litter Size in Purebred and Crossbred Iberian Pigs.

INGA FOOD S. A., as a Spanish company that produces and commercializes fattened pigs, has produced a hybrid Iberian sow called CASTÚA by crossing the Retinto and Entrepelado varieties. The selection of the parental populations is based on selection criteria calculated from purebred information, under the assumption that the genetic correlation between purebred and crossbred performance is high; however, these correlations can be less than one because of a GxE interaction or the presence of non-additive genetic effects. This study estimated the additive and dominance variances of the purebred and crossbred populations for litter size, and calculated the additive genetic correlations between the purebred and crossbred performances. The dataset consisted of 2030 litters from the Entrepelado population, 1977 litters from the Retinto population, and 1958 litters from the crossbred population. The individuals were genotyped with a GeneSeek® GGP Porcine70K HDchip. The model of analysis was a 'biological' multivariate mixed model that included additive and dominance SNP effects. The estimates of the additive genotypic variance for the total number born (TNB) were 0.248, 0.282 and 0.546 for the Entrepelado, Retinto and Crossbred populations, respectively. The estimates of the dominance genotypic variances were 0.177, 0.172 and 0.262 for the Entrepelado, Retinto and Crossbred populations. The results for the number born alive (NBA) were similar. The genetic correlations between the purebred and crossbred performance for TNB and NBA-between the brackets-were 0.663 in the Entrepelado and 0.881 in Retinto poplulations. After backsolving to obtain estimates of the SNP effects, the additive genetic variance associated with genomic regions containing 30 SNPs was estimated, and we identified four genomic regions that each explained > 2% of the additive genetic variance in chromosomes (SSC) 6, 8 and 12: one region in SSC6, two regions in SSC8, and one region in SSC12.

A cross-specific multiplicative binomial recursive model for the analysis of perinatal mortality in a diallel cross among three varieties of Iberian pig.

Perinatal piglet mortality is an important factor in pig production from economic and animal welfare perspectives; however, the statistical analysis of mortality is difficult because of its categorical nature. Recent studies have suggested that a binomial model for the survival of each specific piglet with a logit approach is appropriate and that recursive relationships between traits are useful for taking into account non-genetic relationships with other traits. In this study, the recursive binomial model is expanded in two directions: (1) the recursive phenotypic dependence among traits is allowed to vary among groups of individuals or crosses, and (2) the binomial distribution is replaced by the multiplicative binomial distribution to account for over or underdispersion. In this study, five recursive multiplicative binomial models were used to obtain estimates of the Dickerson crossbreeding parameters in a diallel cross among three varieties of Iberian pigs [Entrepelado (EE), Torbiscal (TT), and Retinto (RR)]. Records (10,255) from 2110 sows were distributed as follows: EE (433 records, 100 sows), ER (2336, 527), ET (942, 177), RE (806, 196), RR (870, 175), RT (2450, 488), TE (193, 36), TR (1993, 359), and TT (232, 68). Average litter size [Total Number Born (TNB)] and number of stillborns (SB) were 8.46 ± 2.27 and 0.25 ± 0.72, respectively. The overdispersion was evident with all models. The model with the best fit included a linear recursive relationship between TNB and the logit of [Formula: see text] of the multiplicative binomial distribution, and it implies that piglet mortality increases with litter size. Estimates of direct effects showed small differences among populations. The analysis of maternal effects indicated that the dams whose mothers were EE had a larger SB, while dams with RR mothers reduced the probability of born dead. The posterior estimates of heterosis suggested a reduction in SB when the sow is crosbred. The multiplicative binomial distribution provides a useful alternative to the binomial distribution when there is overdispersion in the data. Recursive models can be used for modeling non-genetic relationships between traits, even if the phenotypic dependency between traits varies among environments or groups of individuals. Piglet perinatal mortality increased with TNB and is reduced by maternal heterosis.

Maternal Transmission Ratio Distortion in Two Iberian Pig Varieties.

Transmission ratio distortion (TRD) is defined as the allele transmission deviation from the heterozygous parent to the offspring from the expected Mendelian genotypic frequencies. Although TRD can be a confounding factor in genetic mapping studies, this phenomenon remains mostly unknown in pigs, particularly in traditional breeds (i.e., the Iberian pig). We aimed to describe the maternal TRD prevalence and its genomic distribution in two Iberian varieties. Genotypes from a total of 247 families (dam and offspring) of Entrepelado (n = 129) and Retinto (n = 118) Iberian varieties were analyzed. The offspring were sired by both ungenotyped purebred Retinto and Entrepelado Iberian boars, regardless of the dam variety used. After quality control, 16,246 single-nucleotide polymorphisms (SNPs) in the Entrepelado variety and 9744 SNPs in the Retinto variety were analyzed. Maternal TRD was evaluated by a likelihood ratio test under SNP-by-SNP, adapting a previous model solved by Bayesian inference. Results provided 68 maternal TRD loci (TRDLs) in the Entrepelado variety and 24 in the Retinto variety (q < 0.05), with mostly negative TRD values, increasing the transmission of the minor allele. In addition, both varieties shared ten common TRDLs. No strong evidence of biological effects was found in genes with TRDLs. However, some biological processes could be affected by TRDLs, such as embryogenesis at different levels and lipid metabolism. These findings could provide useful insight into the genetic mechanisms to improve the swine industry, particularly in traditional breeds.

A multi-objective approach for designing optimized operation sequence on binary image processing.

In binary image segmentation, the choice of the order of the operation sequence may yield to suboptimal results. In this work, we propose to tackle the associated optimization problem via multi-objective approach. Given the original image, in combination with a list of morphological, logical and stacking operations, the goal is to obtain the ideal output at the lowest computational cost. We compared the performance of two Multi-objective Evolutionary Algorithms (MOEAs): the Non-dominated Sorting Genetic Algorithm (NSGA-II) and the Strength Pareto Evolutionary Algorithm 2 (SPEA2). NSGA-II has better results in most cases, but the difference does not reach statistical significance. The results show that the similarity measure and the computational cost are objective functions in conflict, while the number of operations available and type of input images impact on the quality of Pareto set.

Effect of the genetic line and oleic acid-enriched mixed diets on the subcutaneous fatty acid composition and sensory characteristics of dry-cured shoulders from Iberian pig.

The aim of this study was to research the effect of the genetic background (Retinto, Torbiscal, and their reciprocal crosses) on the subcutaneous fatty acids and the sensory characteristics of dry-cured shoulders from Iberian pig, and also to investigate whether there is some interaction between genotype and diet composition when pigs are reared indoors, to obtain information to improve the selection strategies for purebred Iberian pig. The genetic background affected both the fatty acid composition (C17:0, C17:1 n-7, C18:3 n-3 and C20:0 were significantly different) and the sensory characteristics (marbling, lean fibrousness, and flavour intensity and persistence were significantly influenced), which indicates that they should be taken into account in the selection strategies for purebred Iberian pig. In a similar way, the genotype × diet composition interaction also should be taken into account when selecting a genetic line or cross to be fed indoors on a particular diet because of its repercussion on the sensory characteristics.

Computational Inference of Gene Co-Expression Networks for the identification of Lung Carcinoma Biomarkers: An Ensemble Approach.

Gene Networks (GN), have emerged as an useful tool in recent years for the analysis of different diseases in the field of biomedicine. In particular, GNs have been widely applied for the study and analysis of different types of cancer. In this context, Lung carcinoma is among the most common cancer types and its short life expectancy is partly due to late diagnosis. For this reason, lung cancer biomarkers that can be easily measured are highly demanded in biomedical research. In this work, we present an application of gene co-expression networks in the modelling of lung cancer gene regulatory networks, which ultimately served to the discovery of new biomarkers. For this, a robust GN inference was performed from microarray data concomitantly using three different co-expression measures. Results identified a major cluster of genes involved in SRP-dependent co-translational protein target to membrane, as well as a set of 28 genes that were exclusively found in networks generated from cancer samples. Amongst potential biomarkers, genes N C K A P 1 L and D M D are highlighted due to their implications in a considerable portion of lung and bronchus primary carcinomas. These findings demonstrate the potential of GN reconstruction in the rational prediction of biomarkers.

Comparing the mRNA expression profile and the genetic determinism of intramuscular fat traits in the porcine gluteus medius and longissimus dorsi muscles.

BACKGROUND: Intramuscular fat (IMF) content and composition have a strong impact on the nutritional and organoleptic properties of porcine meat. The goal of the current work was to compare the patterns of gene expression and the genetic determinism of IMF traits in the porcine gluteus medius (GM) and longissimus dorsi (LD) muscles. RESULTS: A comparative analysis of the mRNA expression profiles of the pig GM and LD muscles in 16 Duroc pigs with available microarray mRNA expression measurements revealed the existence of 106 differentially expressed probes (fold-change > 1.5 and q-value < 0.05). Amongst the genes displaying the most significant differential expression, several loci belonging to the Hox transcription factor family were either upregulated (HOXA9, HOXA10, HOXB6, HOXB7 and TBX1) or downregulated (ARX) in the GM muscle. Differences in the expression of genes with key roles in carbohydrate and lipid metabolism (e.g. FABP3, ORMDL1 and SLC37A1) were also detected. By performing a GWAS for IMF content and composition traits recorded in the LD and GM muscles of 350 Duroc pigs, we identified the existence of one region on SSC14 (110-114 Mb) displaying significant associations with C18:0, C18:1(n-7), saturated and unsaturated fatty acid contents in both GM and LD muscles. Moreover, we detected several genome-wide significant associations that were not consistently found in both muscles. Further studies should be performed to confirm whether these associations are muscle-specific. Finally, the performance of an eQTL scan for 74 genes, located within GM QTL regions and with available microarray measurements of gene expression, made possible to identify 14 cis-eQTL regulating the expression of 14 loci, and six of them were confirmed by RNA-Seq. CONCLUSIONS: We have detected significant differences in the mRNA expression patterns of the porcine LD and GM muscles, evidencing that the transcriptomic profile of the skeletal muscle tissue is affected by anatomical, metabolic and functional factors. A highly significant association with IMF composition on SSC14 was replicated in both muscles, highlighting the existence of a common genetic determinism, but we also observed the existence of a few associations whose magnitude and significance varied between LD and GM muscles.

Entropy and Contrast Enhancement of Infrared Thermal Images Using the Multiscale Top-Hat Transform.

Discrete entropy is used to measure the content of an image, where a higher value indicates an image with richer details. Infrared images are capable of revealing important hidden targets. The disadvantage of this type of image is that their low contrast and level of detail are not consistent with human visual perception. These problems can be caused by variations of the environment or by limitations of the cameras that capture the images. In this work we propose a method that improves the details of infrared images, increasing their entropy, preserving their natural appearance, and enhancing contrast. The proposed method extracts multiple features of brightness and darkness from the infrared image. This is done by means of the multiscale top-hat transform. To improve the infrared image, multiple scales are added to the bright areas and multiple areas of darkness are subtracted. The method was tested with 450 infrared thermal images from a public database. Evaluation of the experimental results shows that the proposed method improves the details of the image by increasing entropy, also preserving natural appearance and enhancing the contrast of infrared thermal images.

Servidor de Terminología Médica para el Hospital de Clínicas de Paraguay utilizando Apache Lucene / Medical Terminology Server for the Hospital de Clínicas de Paraguay using Apache Lucene

Introducción: En el Hospital de Clínicas de Paraguay, el proceso actual de búsqueda de terminologías para la codificación médica en estándares de salud toma mucho tiempo ya que se realiza manualmente. Se propone, optimizar el proceso actual de búsqueda a través de la implementación de un servidor de terminología médica utilizando servicios web y una librería de motor de búsqueda de texto. Método: Se propone una arquitectura cliente - servidor de tres capas (también conocida como arquitectura multi-nivel), organizada de la siguiente manera: capa de presentación, de negocios y capa de datos. Se eligió utilizar este patrón por la independencia entre las capas y la clara definición de cada una de ellas en cuanto al objetivo que persigue. El servidor de terminología se encuentra representado en la capa de negocios. Está compuesta por un conjunto de servicios web de tipo REST y una librería de motor de búsqueda de texto, denominada Apache Lucene. Experimentos y Resultados: Fueron realizados dos experimentos acordes a los objetivos específicos mencionados anteriormente. El servidor de terminología implementado responde hasta 19 veces más rápido que el proceso actual de búsqueda y resultó ser bastante competitivo contra Metamorphosys. Si bien ambas herramientas presentan un tiempo de respuesta promedio similar, el servidor de terminología es hasta 5 veces más rápido que Metamorphosys en sus valores atípicos. Conclusiones: El servidor de terminología implementado reduce el tiempo de búsqueda del proceso actual siendo más rápido que el proceso actual de búsqueda. Finalmente, ante la comparación del servidor implementado contra el buscador Metamorphosys, el servidor implementado se muestra competitivo contra dicho buscador ya que tienen tiempos de respuesta similares(AU)

Introduction: In the Hospital Clínicas of Paraguay, the current process of searching for terminologies for medical coding in health standards takes a long time since it is done manually. It is proposed to optimize the current search process through the implementation of a medical terminology server using web services and a text search engine library. Method: Three layer client-server architecture is proposed (also known as multilevel architecture), organized as follows: presentation layer, business layer and data layer. The use of this pattern was due to its contribution to the independence between the layers and the clear definition of them in terms of the objective pursued. The terminology server is represented in the business layer. It is composed of a set of REST web services and a text search engine library, called Apache Lucene. Experiments and Results: Two experiments were carried out according to the objective mentioned above. The implemented terminology server responds up to 19 times faster than the current search process and proved to be quite competitive against Metamorphosys. While both tools have a similar average response time, the terminology server is up to 5 times faster than Metamorphosys in their outliers. Conclusions: The terminology server implemented reduces the search time of the current process being faster than the current search process. Finally, before the comparison of the server implemented against the Metamorphosys search engine, the implemented server is competitive since they have similar response times(AU).

Servidor de Terminología Médica para el Hospital de Clínicas de Paraguay utilizando Apache Lucene / Medical Terminology Server for the Hospital de Clínicas de Paraguay using Apache Lucene

Introducción: En el Hospital de Clínicas de Paraguay, el proceso actual de búsqueda de terminologías para la codificación médica en estándares de salud toma mucho tiempo ya que se realiza manualmente. Se propone, optimizar el proceso actual de búsqueda a través de la implementación de un servidor de terminología médica utilizando servicios web y una librería de motor de búsqueda de texto. Método: Se propone una arquitectura cliente - servidor de tres capas (también conocida como arquitectura multi-nivel), organizada de la siguiente manera: capa de presentación, de negocios y capa de datos. Se eligió utilizar este patrón por la independencia entre las capas y la clara definición de cada una de ellas en cuanto al objetivo que persigue. El servidor de terminología se encuentra representado en la capa de negocios. Está compuesta por un conjunto de servicios web de tipo REST y una librería de motor de búsqueda de texto, denominada Apache Lucene. Experimentos y Resultados: Fueron realizados dos experimentos acordes a los objetivos específicos mencionados anteriormente. El servidor de terminología implementado responde hasta 19 veces más rápido que el proceso actual de búsqueda y resultó ser bastante competitivo contra Metamorphosys. Si bien ambas herramientas presentan un tiempo de respuesta promedio similar, el servidor de terminología es hasta 5 veces más rápido que Metamorphosys en sus valores atípicos. Conclusiones: El servidor de terminología implementado reduce el tiempo de búsqueda del proceso actual siendo más rápido que el proceso actual de búsqueda. Finalmente, ante la comparación del servidor implementado contra el buscador Metamorphosys, el servidor implementado se muestra competitivo contra dicho buscador ya que tienen tiempos de respuesta similares(AU)

Introduction: In the Hospital Clínicas of Paraguay, the current process of searching for terminologies for medical coding in health standards takes a long time since it is done manually. It is proposed to optimize the current search process through the implementation of a medical terminology server using web services and a text search engine library. Method: Three layer client-server architecture is proposed (also known as multilevel architecture), organized as follows: presentation layer, business layer and data layer. The use of this pattern was due to its contribution to the independence between the layers and the clear definition of them in terms of the objective pursued. The terminology server is represented in the business layer. It is composed of a set of REST web services and a text search engine library, called Apache Lucene. Experiments and Results: Two experiments were carried out according to the objective mentioned above. The implemented terminology server responds up to 19 times faster than the current search process and proved to be quite competitive against Metamorphosys. While both tools have a similar average response time, the terminology server is up to 5 times faster than Metamorphosys in their outliers. Conclusions: The terminology server implemented reduces the search time of the current process being faster than the current search process. Finally, before the comparison of the server implemented against the Metamorphosys search engine, the implemented server is competitive since they have similar response times(AU)

Structure Optimization for Large Gene Networks Based on Greedy Strategy.

In the last few years, gene networks have become one of most important tools to model biological processes. Among other utilities, these networks visually show biological relationships between genes. However, due to the large amount of the currently generated genetic data, their size has grown to the point of being unmanageable. To solve this problem, it is possible to use computational approaches, such as heuristics-based methods, to analyze and optimize gene network's structure by pruning irrelevant relationships. In this paper we present a new method, called GeSOp, to optimize large gene network structures. The method is able to perform a considerably prune of the irrelevant relationships comprising the input network. To do so, the method is based on a greedy heuristic to obtain the most relevant subnetwork. The performance of our method was tested by means of two experiments on gene networks obtained from different organisms. The first experiment shows how GeSOp is able not only to carry out a significant reduction in the size of the network, but also to maintain the biological information ratio. In the second experiment, the ability to improve the biological indicators of the network is checked. Hence, the results presented show that GeSOp is a reliable method to optimize and improve the structure of large gene networks.