Universal ontology: attentive tracking of objects and substances across languages and over development

Previous research has demonstrated that adults are successful at visually tracking rigidly moving items, but experience great difficulties when tracking substance-like ‘‘pouring’’ items. Using a comparative approach, we investigated whether the presence/absence of the grammatical count–mass distinction influences adults and children’s ability to attentively track objects versus substances. More specifically, we aimed to explore whether the higher success at tracking rigid over substance-like items appears universally or whether speakers of classifier languages (like Japanese, not marking the object–substance distinction) are advantaged at tracking substances as compared to speakers of non-classifier languages (like Swiss German, marking the object–substance distinction). Our results supported the idea that language has no effect on low-level cognitive processes such as the attentive visual processing of objects and substances. We concluded arguing that the tendency to prioritize objects is universal and independent of specific characteristics of the language spoken.

The Intestinal Microbiota Contributes to the Ability of Helminths to Modulate Allergic Inflammation.

Intestinal helminths are potent regulators of their host's immune system and can ameliorate inflammatory diseases such as allergic asthma. In the present study we have assessed whether this anti-inflammatory activity was purely intrinsic to helminths, or whether it also involved crosstalk with the local microbiota. We report that chronic infection with the murine helminth Heligmosomoides polygyrus bakeri (Hpb) altered the intestinal habitat, allowing increased short chain fatty acid (SCFA) production. Transfer of the Hpb-modified microbiota alone was sufficient to mediate protection against allergic asthma. The helminth-induced anti-inflammatory cytokine secretion and regulatory T cell suppressor activity that mediated the protection required the G protein-coupled receptor (GPR)-41. A similar alteration in the metabolic potential of intestinal bacterial communities was observed with diverse parasitic and host species, suggesting that this represents an evolutionary conserved mechanism of host-microbe-helminth interactions.

Predicting 5- and 10-year survival in older women with early-stage breast cancer: self-rated health and walking ability.

OBJECTIVES To determine life expectancy for older women with breast cancer. DESIGN Prospective longitudinal study with 10 years of follow-up data. SETTING Hospitals or collaborating tumor registries in four geographic regions (Los Angeles, California; Minnesota; North Carolina; Rhode Island). PARTICIPANTS Women aged 65 and older at time of breast cancer diagnosis with Stage I to IIIA disease with measures of self-rated health (SRH) and walking ability at baseline (N = 615; 17% aged ≥80, 52% Stage I, 58% with ≥2 comorbidities). MEASUREMENTS Baseline SRH, baseline self-reported walking ability, all-cause and breast cancer-specific estimated probability of 5- and 10-year survival. RESULTS At the time of breast cancer diagnosis, 39% of women reported poor SRH, and 28% reported limited ability to walk several blocks. The all-cause survival curves appear to separate after approximately 3 years, and the difference in survival probability between those with low SRH and limited walking ability and those with high SRH and no walking ability limitation was significant (0.708 vs 0.855 at 5 years, P ≤ .001; 0.300 vs 0.648 at 10 years, P < .001). There were no differences between the groups in breast cancer-specific survival at 5 and 10 years (P = .66 at 5 years, P = .16 at 10 years). CONCLUSION The combination of low SRH and limited ability to walk several blocks at diagnosis is an important predictor of worse all-cause survival at 5 and 10 years. These self-report measures easily assessed in clinical practice may be an effective strategy to improve treatment decision-making in older adults with cancer.

Ability to Work and Employment Rates in Human Immunodeficiency Virus (HIV)-1-Infected Individuals Receiving Combination Antiretroviral Therapy: The Swiss HIV Cohort Study

Background.  Limited data exist on human immunodeficiency virus (HIV)-infected individuals' ability to work after receiving combination antiretroviral therapy (cART). We aimed to investigate predictors of regaining full ability to work at 1 year after starting cART. Methods.  Antiretroviral-naive HIV-infected individuals <60 years who started cART from January 1998 through December 2012 within the framework of the Swiss HIV Cohort Study were analyzed. Inability to work was defined as a medical judgment of the patient's ability to work as 0%. Results.  Of 5800 subjects, 4382 (75.6%) were fully able to work, 471 (8.1%) able to work part time, and 947 (16.3%) were unable to work at baseline. Of the 947 patients unable to work, 439 (46.3%) were able to work either full time or part time at 1 year of treatment. Predictors of recovering full ability to work were non-white ethnicity (odds ratio [OR], 2.06; 95% confidence interval [CI], 1.20-3.54), higher education (OR, 4.03; 95% CI, 2.47-7.48), and achieving HIV-ribonucleic acid <50 copies/mL (OR, 1.83; 95% CI, 1.20-2.80). Older age (OR, 0.55; 95% CI, .42-.72, per 10 years older) and psychiatric disorders (OR, 0.24; 95% CI, .13-.47) were associated with lower odds of ability to work. Recovering full ability to work at 1 year increased from 24.0% in 1998-2001 to 41.2% in 2009-2012, but the employment rates did not increase. Conclusions.  Regaining full ability to work depends primarily on achieving viral suppression, absence of psychiatric comorbidity, and favorable psychosocial factors. The discrepancy between patients' ability to work and employment rates indicates barriers to reintegration of persons infected with HIV.

The information bottleneck method for genome-wide association studies

In population studies, most current methods focus on identifying one outcome-related SNP at a time by testing for differences of genotype frequencies between disease and healthy groups or among different population groups. However, testing a great number of SNPs simultaneously has a problem of multiple testing and will give false-positive results. Although, this problem can be effectively dealt with through several approaches such as Bonferroni correction, permutation testing and false discovery rates, patterns of the joint effects by several genes, each with weak effect, might not be able to be determined. With the availability of high-throughput genotyping technology, searching for multiple scattered SNPs over the whole genome and modeling their joint effect on the target variable has become possible. Exhaustive search of all SNP subsets is computationally infeasible for millions of SNPs in a genome-wide study. Several effective feature selection methods combined with classification functions have been proposed to search for an optimal SNP subset among big data sets where the number of feature SNPs far exceeds the number of observations. ^ In this study, we take two steps to achieve the goal. First we selected 1000 SNPs through an effective filter method and then we performed a feature selection wrapped around a classifier to identify an optimal SNP subset for predicting disease. And also we developed a novel classification method-sequential information bottleneck method wrapped inside different search algorithms to identify an optimal subset of SNPs for classifying the outcome variable. This new method was compared with the classical linear discriminant analysis in terms of classification performance. Finally, we performed chi-square test to look at the relationship between each SNP and disease from another point of view. ^ In general, our results show that filtering features using harmononic mean of sensitivity and specificity(HMSS) through linear discriminant analysis (LDA) is better than using LDA training accuracy or mutual information in our study. Our results also demonstrate that exhaustive search of a small subset with one SNP, two SNPs or 3 SNP subset based on best 100 composite 2-SNPs can find an optimal subset and further inclusion of more SNPs through heuristic algorithm doesn't always increase the performance of SNP subsets. Although sequential forward floating selection can be applied to prevent from the nesting effect of forward selection, it does not always out-perform the latter due to overfitting from observing more complex subset states. ^ Our results also indicate that HMSS as a criterion to evaluate the classification ability of a function can be used in imbalanced data without modifying the original dataset as against classification accuracy. Our four studies suggest that Sequential Information Bottleneck(sIB), a new unsupervised technique, can be adopted to predict the outcome and its ability to detect the target status is superior to the traditional LDA in the study. ^ From our results we can see that the best test probability-HMSS for predicting CVD, stroke,CAD and psoriasis through sIB is 0.59406, 0.641815, 0.645315 and 0.678658, respectively. In terms of group prediction accuracy, the highest test accuracy of sIB for diagnosing a normal status among controls can reach 0.708999, 0.863216, 0.639918 and 0.850275 respectively in the four studies if the test accuracy among cases is required to be not less than 0.4. On the other hand, the highest test accuracy of sIB for diagnosing a disease among cases can reach 0.748644, 0.789916, 0.705701 and 0.749436 respectively in the four studies if the test accuracy among controls is required to be at least 0.4. ^ A further genome-wide association study through Chi square test shows that there are no significant SNPs detected at the cut-off level 9.09451E-08 in the Framingham heart study of CVD. Study results in WTCCC can only detect two significant SNPs that are associated with CAD. In the genome-wide study of psoriasis most of top 20 SNP markers with impressive classification accuracy are also significantly associated with the disease through chi-square test at the cut-off value 1.11E-07. ^ Although our classification methods can achieve high accuracy in the study, complete descriptions of those classification results(95% confidence interval or statistical test of differences) require more cost-effective methods or efficient computing system, both of which can't be accomplished currently in our genome-wide study. We should also note that the purpose of this study is to identify subsets of SNPs with high prediction ability and those SNPs with good discriminant power are not necessary to be causal markers for the disease.^

Design, synthesis and cellular and molecular pharmacology of 2$\sp\prime$-bromo-4$\sp\prime$-epidaunorubicin (WP401): A novel non-cross-resistant anthracycline antibiotic with the intrinsic ability to circumvent P -glycoprotein-mediated drug resistance

We designed and synthesized a novel daunorubicin (DNR) analogue that effectively circumvents P-glycoprotein (P-gp)-mediated drug resistance. The fully protected carbohydrate intermediate 1,2-dibromoacosamine was prepared from acosamine and effectively coupled to daunomycinone in high yield. Deprotection under alkaline conditions yielded 2$\sp\prime$-bromo-4$\sp\prime$-epidaunorubicin (WP401). The in vitro cytotoxicity and cellular and molecular pharmacology of WP401 were compared with those of DNR in a panel of wild-type cell lines (KB-3-1, P388S, and HL60S) and their multidrug-resistant (MDR) counterparts (KB-V1, P388/DOX, and HL60/DOX). Fluorescent spectrophotometry, flow cytometry, and confocal laser scanning microscopy were used to measure intracellular accumulation, retention, and subcellular distribution of these agents. All MDR cell lines exhibited reduced DNR uptake that was restored, upon incubation with either verapamil (VER) or cyclosporin A (CSA), to the level found in sensitive cell lines. In contrast, the uptake of WP401 was essentially the same in the absence or presence of VER or CSA in all tested cell lines. The in vitro cytotoxicity of WP401 was similar to that of DNR in the sensitive cell lines but significantly higher in resistant cell lines (resistance index (RI) of 2-6 for WP401 vs 75-85 for DNR). To ascertain whether drug-mediated cytotoxicity and retention were accompanied by DNA strand breaks, DNA single- and double-strand breaks were assessed by alkaline elution. High levels of such breaks were obtained using 0.1-2 $\mu$g/mL of WP401 in both sensitive and resistant cells. In contrast, DNR caused strand breaks only in sensitive cells and not much in resistant cells. We also compared drug-induced DNA fragmentation similar to that induced by DNR. However, in P-gp-positive cells, WP401 induced 2- to 5-fold more DNA fragmentation than DNR. This increased DNA strand breakage by WP401 was correlated with its increased uptake and cytotoxicity in these cell lines. Overall these results indicate that WP401 is more cytotoxic than DNR in MDR cells and that this phenomenon might be related to the reduced basicity of the amino group and increased lipophilicity of WP401. ^

Assessment of the effects of ozone exposure and plant competition on the reproductive ability of three therophytic clover species from Iberian pastures

Ozone (O3) phytototoxicity has been reported on a wide range of crops and wild Central European plantspecies, however no information has been provided regarding the sensitivity of plantspecies from dehesa Mediterranean therophytic grasslands in spite of their great plantspecies richness and the high O3 levels that are recorded in this area. A study was carried out in open-top chambers (OTCs) to assess the effects of O3 and competition on the reproductiveability of threecloverspecies: Trifolium cherleri, Trifolium subterraneum and Trifolium striatum. A phytometer approach was followed, therefore plants of these species were grown in mesoscosms composed of monocultures of four plants of each species, of threeplants of each species competing against a Briza maxima individual or of a single plant of each cloverspecies competing with threeB. maximaplants. Three O3 treatments were adopted: charcoal filtered air (CFA), non-filtered air (NFA) and non-filtered air supplemented with 40 nl l−1 of O3 (NFA+). The different mesocosms were exposed to the different O3 treatments for 45 days and then they remained in the open. Ozoneexposure caused reductions in the flower biomass of the threecloverspecies assessed. In the case of T. cherleri and T. subterraneum this effect was found following their exposure to the different O3 treatments during their vegetative period. An attenuation of these effects was found when the plants remained in the open. Ozone-induced detrimental effects on the seed output of T. striatum were also observed. The flower biomass of the cloverplants grown in monocultures was greater than when competing with one or threeB. maxima individuals. An increased flower biomass was found in the CFA monoculture mesocosms of T. cherleri when compared with the remaining mesocosms, once the plants were exposed in the open for 60 days. The implications of these effects on the performance of dehesa acid grasslands and for the definition of O3 critical levels is discussed

Real Time FPGA Implementation of an Modulation Classifier for Electronic Warfare Applications

This paper presents an automatic modulation classifier for electronic warfare applications. It is a pattern recognition modulation classifier based on statistical features of the phase and instantaneous frequency. This classifier runs in a real time operation mode with sampling rates in excess of 1 Gsample/s. The hardware platform for this application is a Field Programmable Gate Array (FPGA). This AMC is subsidiary of a digital channelised receiver also implemented in the same platform.

Dispersal of aphids, whiteflies and their natural enemies under photoselective nets

Integrated Pest Management of insects includes several control tactics, such as the use of photoselective nets, which may reduce the flight activity of insects. Limiting the dispersal of pests such as aphids and whiteflies is important because of their major role as vectors of plant viruses, while a minor impact on natural enemies is desired. In this study, we examined for the first time the dispersal ability of three vector species, Bemisia tabaci (Gennadius) (Hemiptera: Aleyrodidae), Macrosiphum euphorbiae (Thomas) (Hemiptera: Aphididae) and Myzus persicae (Sulzer) (Hemiptera: Aphididae), in cages covered with photoselective nets. Contrary to the results obtained with aphids, the ability of the whitefly B. tabaci, to reach the target plant was reduced by photoselective nets. In a second set of experiments, the impact of UV-absorbing nets on the visual cues of two important predator species, Orius laevigatus (Fieber) (Hemiptera: Anthocoridae) and Amblyseius swirskii Athias-Henriot (Acari: Phytoseiidae), was evaluated. The anthocorid was caught in higher numbers in traps placed under regular nets, whereas the mites preferably chose environments in which the UV radiation was attenuated. We have observed a wide range of effects that impedes generalization, although photoselective nets have a positive effect on pest management of whiteflies and aphids under protected environments.

Collaborative data stream mining in ubiquitous environments using dynamic classifier selection

In ubiquitous data stream mining applications, different devices often aim to learn concepts that are similar to some extent. In these applications, such as spam filtering or news recommendation, the data stream underlying concept (e.g., interesting mail/news) is likely to change over time. Therefore, the resultant model must be continuously adapted to such changes. This paper presents a novel Collaborative Data Stream Mining (Coll-Stream) approach that explores the similarities in the knowledge available from other devices to improve local classification accuracy. Coll-Stream integrates the community knowledge using an ensemble method where the classifiers are selected and weighted based on their local accuracy for different partitions of the feature space. We evaluate Coll-Stream classification accuracy in situations with concept drift, noise, partition granularity and concept similarity in relation to the local underlying concept. The experimental results show that Coll-Stream resultant model achieves stability and accuracy in a variety of situations using both synthetic and real world datasets.

Towards a fuzzy-based multi-classifier selection module for activity recognition applications

Performing activity recognition using the information provided by the different sensors embedded in a smartphone face limitations due to the capabilities of those devices when the computations are carried out in the terminal. In this work a fuzzy inference module is implemented in order to decide which classifier is the most appropriate to be used at a specific moment regarding the application requirements and the device context characterized by its battery level, available memory and CPU load. The set of classifiers that is considered is composed of Decision Tables and Trees that have been trained using different number of sensors and features. In addition, some classifiers perform activity recognition regardless of the on-body device position and others rely on the previous recognition of that position to use a classifier that is trained with measurements gathered with the mobile placed on that specific position. The modules implemented show that an evaluation of the classifiers allows sorting them so the fuzzy inference module can choose periodically the one that best suits the device context and application requirements.

Contributions to Bayesian network learning with applications to neuroscience

Neuronal morphology is a key feature in the study of brain circuits, as it is highly related to information processing and functional identification. Neuronal morphology affects the process of integration of inputs from other neurons and determines the neurons which receive the output of the neurons. Different parts of the neurons can operate semi-independently according to the spatial location of the synaptic connections. As a result, there is considerable interest in the analysis of the microanatomy of nervous cells since it constitutes an excellent tool for better understanding cortical function. However, the morphologies, molecular features and electrophysiological properties of neuronal cells are extremely variable. Except for some special cases, this variability makes it hard to find a set of features that unambiguously define a neuronal type. In addition, there are distinct types of neurons in particular regions of the brain. This morphological variability makes the analysis and modeling of neuronal morphology a challenge. Uncertainty is a key feature in many complex real-world problems. Probability theory provides a framework for modeling and reasoning with uncertainty. Probabilistic graphical models combine statistical theory and graph theory to provide a tool for managing domains with uncertainty. In particular, we focus on Bayesian networks, the most commonly used probabilistic graphical model. In this dissertation, we design new methods for learning Bayesian networks and apply them to the problem of modeling and analyzing morphological data from neurons. The morphology of a neuron can be quantified using a number of measurements, e.g., the length of the dendrites and the axon, the number of bifurcations, the direction of the dendrites and the axon, etc. These measurements can be modeled as discrete or continuous data. The continuous data can be linear (e.g., the length or the width of a dendrite) or directional (e.g., the direction of the axon). These data may follow complex probability distributions and may not fit any known parametric distribution. Modeling this kind of problems using hybrid Bayesian networks with discrete, linear and directional variables poses a number of challenges regarding learning from data, inference, etc. In this dissertation, we propose a method for modeling and simulating basal dendritic trees from pyramidal neurons using Bayesian networks to capture the interactions between the variables in the problem domain. A complete set of variables is measured from the dendrites, and a learning algorithm is applied to find the structure and estimate the parameters of the probability distributions included in the Bayesian networks. Then, a simulation algorithm is used to build the virtual dendrites by sampling values from the Bayesian networks, and a thorough evaluation is performed to show the model’s ability to generate realistic dendrites. In this first approach, the variables are discretized so that discrete Bayesian networks can be learned and simulated. Then, we address the problem of learning hybrid Bayesian networks with different kinds of variables. Mixtures of polynomials have been proposed as a way of representing probability densities in hybrid Bayesian networks. We present a method for learning mixtures of polynomials approximations of one-dimensional, multidimensional and conditional probability densities from data. The method is based on basis spline interpolation, where a density is approximated as a linear combination of basis splines. The proposed algorithms are evaluated using artificial datasets. We also use the proposed methods as a non-parametric density estimation technique in Bayesian network classifiers. Next, we address the problem of including directional data in Bayesian networks. These data have some special properties that rule out the use of classical statistics. Therefore, different distributions and statistics, such as the univariate von Mises and the multivariate von Mises–Fisher distributions, should be used to deal with this kind of information. In particular, we extend the naive Bayes classifier to the case where the conditional probability distributions of the predictive variables given the class follow either of these distributions. We consider the simple scenario, where only directional predictive variables are used, and the hybrid case, where discrete, Gaussian and directional distributions are mixed. The classifier decision functions and their decision surfaces are studied at length. Artificial examples are used to illustrate the behavior of the classifiers. The proposed classifiers are empirically evaluated over real datasets. We also study the problem of interneuron classification. An extensive group of experts is asked to classify a set of neurons according to their most prominent anatomical features. A web application is developed to retrieve the experts’ classifications. We compute agreement measures to analyze the consensus between the experts when classifying the neurons. Using Bayesian networks and clustering algorithms on the resulting data, we investigate the suitability of the anatomical terms and neuron types commonly used in the literature. Additionally, we apply supervised learning approaches to automatically classify interneurons using the values of their morphological measurements. Then, a methodology for building a model which captures the opinions of all the experts is presented. First, one Bayesian network is learned for each expert, and we propose an algorithm for clustering Bayesian networks corresponding to experts with similar behaviors. Then, a Bayesian network which represents the opinions of each group of experts is induced. Finally, a consensus Bayesian multinet which models the opinions of the whole group of experts is built. A thorough analysis of the consensus model identifies different behaviors between the experts when classifying the interneurons in the experiment. A set of characterizing morphological traits for the neuronal types can be defined by performing inference in the Bayesian multinet. These findings are used to validate the model and to gain some insights into neuron morphology. Finally, we study a classification problem where the true class label of the training instances is not known. Instead, a set of class labels is available for each instance. This is inspired by the neuron classification problem, where a group of experts is asked to individually provide a class label for each instance. We propose a novel approach for learning Bayesian networks using count vectors which represent the number of experts who selected each class label for each instance. These Bayesian networks are evaluated using artificial datasets from supervised learning problems. Resumen La morfología neuronal es una característica clave en el estudio de los circuitos cerebrales, ya que está altamente relacionada con el procesado de información y con los roles funcionales. La morfología neuronal afecta al proceso de integración de las señales de entrada y determina las neuronas que reciben las salidas de otras neuronas. Las diferentes partes de la neurona pueden operar de forma semi-independiente de acuerdo a la localización espacial de las conexiones sinápticas. Por tanto, existe un interés considerable en el análisis de la microanatomía de las células nerviosas, ya que constituye una excelente herramienta para comprender mejor el funcionamiento de la corteza cerebral. Sin embargo, las propiedades morfológicas, moleculares y electrofisiológicas de las células neuronales son extremadamente variables. Excepto en algunos casos especiales, esta variabilidad morfológica dificulta la definición de un conjunto de características que distingan claramente un tipo neuronal. Además, existen diferentes tipos de neuronas en regiones particulares del cerebro. La variabilidad neuronal hace que el análisis y el modelado de la morfología neuronal sean un importante reto científico. La incertidumbre es una propiedad clave en muchos problemas reales. La teoría de la probabilidad proporciona un marco para modelar y razonar bajo incertidumbre. Los modelos gráficos probabilísticos combinan la teoría estadística y la teoría de grafos con el objetivo de proporcionar una herramienta con la que trabajar bajo incertidumbre. En particular, nos centraremos en las redes bayesianas, el modelo más utilizado dentro de los modelos gráficos probabilísticos. En esta tesis hemos diseñado nuevos métodos para aprender redes bayesianas, inspirados por y aplicados al problema del modelado y análisis de datos morfológicos de neuronas. La morfología de una neurona puede ser cuantificada usando una serie de medidas, por ejemplo, la longitud de las dendritas y el axón, el número de bifurcaciones, la dirección de las dendritas y el axón, etc. Estas medidas pueden ser modeladas como datos continuos o discretos. A su vez, los datos continuos pueden ser lineales (por ejemplo, la longitud o la anchura de una dendrita) o direccionales (por ejemplo, la dirección del axón). Estos datos pueden llegar a seguir distribuciones de probabilidad muy complejas y pueden no ajustarse a ninguna distribución paramétrica conocida. El modelado de este tipo de problemas con redes bayesianas híbridas incluyendo variables discretas, lineales y direccionales presenta una serie de retos en relación al aprendizaje a partir de datos, la inferencia, etc. En esta tesis se propone un método para modelar y simular árboles dendríticos basales de neuronas piramidales usando redes bayesianas para capturar las interacciones entre las variables del problema. Para ello, se mide un amplio conjunto de variables de las dendritas y se aplica un algoritmo de aprendizaje con el que se aprende la estructura y se estiman los parámetros de las distribuciones de probabilidad que constituyen las redes bayesianas. Después, se usa un algoritmo de simulación para construir dendritas virtuales mediante el muestreo de valores de las redes bayesianas. Finalmente, se lleva a cabo una profunda evaluaci ón para verificar la capacidad del modelo a la hora de generar dendritas realistas. En esta primera aproximación, las variables fueron discretizadas para poder aprender y muestrear las redes bayesianas. A continuación, se aborda el problema del aprendizaje de redes bayesianas con diferentes tipos de variables. Las mixturas de polinomios constituyen un método para representar densidades de probabilidad en redes bayesianas híbridas. Presentamos un método para aprender aproximaciones de densidades unidimensionales, multidimensionales y condicionales a partir de datos utilizando mixturas de polinomios. El método se basa en interpolación con splines, que aproxima una densidad como una combinación lineal de splines. Los algoritmos propuestos se evalúan utilizando bases de datos artificiales. Además, las mixturas de polinomios son utilizadas como un método no paramétrico de estimación de densidades para clasificadores basados en redes bayesianas. Después, se estudia el problema de incluir información direccional en redes bayesianas. Este tipo de datos presenta una serie de características especiales que impiden el uso de las técnicas estadísticas clásicas. Por ello, para manejar este tipo de información se deben usar estadísticos y distribuciones de probabilidad específicos, como la distribución univariante von Mises y la distribución multivariante von Mises–Fisher. En concreto, en esta tesis extendemos el clasificador naive Bayes al caso en el que las distribuciones de probabilidad condicionada de las variables predictoras dada la clase siguen alguna de estas distribuciones. Se estudia el caso base, en el que sólo se utilizan variables direccionales, y el caso híbrido, en el que variables discretas, lineales y direccionales aparecen mezcladas. También se estudian los clasificadores desde un punto de vista teórico, derivando sus funciones de decisión y las superficies de decisión asociadas. El comportamiento de los clasificadores se ilustra utilizando bases de datos artificiales. Además, los clasificadores son evaluados empíricamente utilizando bases de datos reales. También se estudia el problema de la clasificación de interneuronas. Desarrollamos una aplicación web que permite a un grupo de expertos clasificar un conjunto de neuronas de acuerdo a sus características morfológicas más destacadas. Se utilizan medidas de concordancia para analizar el consenso entre los expertos a la hora de clasificar las neuronas. Se investiga la idoneidad de los términos anatómicos y de los tipos neuronales utilizados frecuentemente en la literatura a través del análisis de redes bayesianas y la aplicación de algoritmos de clustering. Además, se aplican técnicas de aprendizaje supervisado con el objetivo de clasificar de forma automática las interneuronas a partir de sus valores morfológicos. A continuación, se presenta una metodología para construir un modelo que captura las opiniones de todos los expertos. Primero, se genera una red bayesiana para cada experto y se propone un algoritmo para agrupar las redes bayesianas que se corresponden con expertos con comportamientos similares. Después, se induce una red bayesiana que modela la opinión de cada grupo de expertos. Por último, se construye una multired bayesiana que modela las opiniones del conjunto completo de expertos. El análisis del modelo consensuado permite identificar diferentes comportamientos entre los expertos a la hora de clasificar las neuronas. Además, permite extraer un conjunto de características morfológicas relevantes para cada uno de los tipos neuronales mediante inferencia con la multired bayesiana. Estos descubrimientos se utilizan para validar el modelo y constituyen información relevante acerca de la morfología neuronal. Por último, se estudia un problema de clasificación en el que la etiqueta de clase de los datos de entrenamiento es incierta. En cambio, disponemos de un conjunto de etiquetas para cada instancia. Este problema está inspirado en el problema de la clasificación de neuronas, en el que un grupo de expertos proporciona una etiqueta de clase para cada instancia de manera individual. Se propone un método para aprender redes bayesianas utilizando vectores de cuentas, que representan el número de expertos que seleccionan cada etiqueta de clase para cada instancia. Estas redes bayesianas se evalúan utilizando bases de datos artificiales de problemas de aprendizaje supervisado.