http://hdl.handle.net/2117/3976 2013-06-20T04:26:51Z http://hdl.handle.net/2117/19485 Title: Feature selection for the prediction and visualization of brain tumor types using proton magnetic resonance spectroscopy data Authors: González Navarro, Félix Fernando; Belanche Muñoz, Luis Antonio Abstract: In cancer diagnosis, classification of the different tumor types is of great importance. An accurate prediction of basic tumor types provides better treatment and may minimize the negative impact of incorrectly targeted toxic or aggressive treatments. Moreover, the correct prediction of cancer types in the brain using non-invasive information –e.g. 1H-MRS data– could avoid patients to suffer collateral problems derived from exploration techniques that require surgery. We present a feature selection algorithm that is specially designed to be used in 1H-MRS (Proton Magnetic Resonance Spectroscopy) data of brain tumors. This algorithm takes advantage of the fact that some metabolic levels may consistently present notorious differences between specific tumor types. We present detailed experimental results using an international dataset in which highly attractive models are obtained. The models are evaluated according to their accuracy, simplicity and medical interpretability. We also explore the influence of redundancy in the modelling process. Our results suggest that a moderate amount of redundant metabolites can actually enhance class-separability and therefore accuracy. 2013-06-03T09:16:55Z http://hdl.handle.net/2117/19482 Title: Parsimonious selection of useful genes in microarray gene expression data Authors: González Navarro, Félix Fernando; Belanche Muñoz, Luis Antonio Abstract: Machine Learning methods have of late made significant efforts to solving multidisciplinary problems in the field of cancer classification in microarray gene expression data. These tasks are characterized by a large number of features and a few observations, making the modeling a non-trivial undertaking. In this work we apply entropic filter methods for gene selection, in combination with several off-the-shelf classifiers. The introduction of bootstrap resampling techniques permits the achievement of more stable performance estimates. Our findings show that the proposed methodology permits a drastic reduction in dimension, offering attractive solutions both in terms of prediction accuracy and number of explanatory genes; a dimensionality reduction technique preserving discrimination capabilities is used for visualization of the selected genes. 2013-06-03T08:29:20Z http://hdl.handle.net/2117/18252 Title: Dr. D. Juan A. Subirana : Medio siglo investigando : los orígenes Authors: Subirana Torrent, Juan A. Abstract: Ressenya autobiogràfica 2013-03-13T09:55:13Z http://hdl.handle.net/2117/17987 Title: Intelligent management of sepsis in the intensive care unit Authors: Ribas Ripoll, Vicent; Ruiz Rodríguez, Juan Carlos; Vellido Alcacena, Alfredo 2013-02-26T15:01:43Z http://hdl.handle.net/2117/17986 Title: On the use of graphical models to study ICU outcome prediction in septic patients treated with statins Authors: Ribas, Vicent J.; Caballero López, Jesús; Sáez de Tejada, Anna; Ruiz Rodríguez, Juan Carlos; Ruiz Sanmartin, Adolfo; Rello, Jordi; Vellido Alcacena, Alfredo 2013-02-26T14:24:00Z http://hdl.handle.net/2117/17985 Title: Complementing kernel-based visualization of protein sequences with their phylogenetic tree Authors: Cárdenas, Martha Ivón; Vellido Alcacena, Alfredo; Olier, Iván; Rovira, Xavier; Giraldo, Jesus 2013-02-26T14:17:25Z http://hdl.handle.net/2117/17984 Title: Discovering hidden pathways in bioinformatics Authors: Lisboa, Paulo J.G.; Jarman, Ian H.; Etchells, Terence A.; Chambers, Simon J.; Bacciu, Davide; Whittaker, Joe; Garibaldi, Jon M.; Ortega Martorell, Sandra; Vellido Alcacena, Alfredo; Ellis, Ian O. 2013-02-26T14:08:36Z http://hdl.handle.net/2117/17939 Title: Statistical approaches for modeling in microbial source tracking Authors: Belanche Muñoz, Luis Antonio; Blanch, Anicet R. Abstract: Microbial source tracking (MST) concerns the definition of new indicators and appropriate detection methods, the identification of host-specific indicators of fecal pollution, and ultimately the development of useful and reliable predictive models for practical deployment. Optimal predictive models should be designed using proper statistical and computational tools for the analysis of the available data samples. A further requirement is found in the determination of appropriate sets of predictors (indicators, tracers) for developing accurate and low-cost MST solutions. This chapter briefly reviews some of these modeling tools, and their use and feasibility in providing more accurate MST-based results. It also evaluates the potential of established and new algorithmic methods to the identification of fecal pollution sources. 2013-02-22T12:56:43Z http://hdl.handle.net/2117/17935 Title: Learning with heterogeneous neural networks Authors: Belanche Muñoz, Luis Antonio Abstract: This chapter studies a class of neuron models that computes a user-defined similarity function between inputs and weights. The neuron transfer function is formed by composition of an adapted logistic function with the quasi-linear mean of the partial input-weight similarities. The neuron model is capable of dealing directly with mixtures of continuous as well as discrete quantities, among other data types and there is provision for missing values. An artificial neural network using these neuron models is trained using a breeder genetic algorithm until convergence. A number of experiments are carried out in several real-world problems in very different application domains described by mixtures of variales of distinct types and eventually showing missing values. This heterogeneous network is compared to a standard radial basis function network and to a multi-layer perceptron networks and shown to learn from with superior generalization ability at a comparable computational cost. A further important advantage of the resulting neural solutions is the great interpretability of the learned weights, which is done in terms of weighted similarities to prototypes. 2013-02-22T12:18:30Z http://hdl.handle.net/2117/17284 Title: Preprocessing MRS information for classification of human brain tumours Authors: Arizmendi Pereira, Carlos Julio; Vellido Alcacena, Alfredo; Romero Merino, Enrique 2013-01-11T15:18:55Z http://hdl.handle.net/2117/17282 Title: Kernel generative topographic mapping of protein sequences Authors: Cárdenas, Martha Ivón; Vellido Alcacena, Alfredo; Olier Caparroso, Iván; Rovira, Xavier; Giraldo, Jesús 2013-01-11T14:57:40Z http://hdl.handle.net/2117/17023 Title: A logic programming approach to parsing and production in fluid construction grammar Authors: Sierra Santibáñez, Josefina Abstract: This paper presents a Logic Programming approach to parsing and production in Fluid Construction Grammar (FCG). It builds on previous work on the formalisation of FCG in terms of First Order Logic (FOL) concepts, more specifically on the definition of its core inference operations, unification and merge, in terms of FOL unification and search in the space of a particular set of FOL terms called structure arrangements. An implementation of such inference operations based on Logic Programming and Artificial Intelligence techniques such as unification and heuristic search is outlined. 2012-11-26T15:45:23Z http://hdl.handle.net/2117/15684 Title: Machine learning in human cancer research Authors: Vellido Alcacena, Alfredo; Lisboa, Paulo J.G. Abstract: Evidence-based medicine has grown in stature over three decades and is now regarded a key development of modern medicine. The evidence base can be heterogeneous, involving both qualitative knowledge and measured quantitative data. Data analysis in the area of cancer research has for long been the playing field of statisticians but, over the last decade, Machine Learning (ML) methods have also begun to establish themselves an an alternative and promising approach to computer-based data analysis in oncology. In this chapter, we provide a state-of-the-art in the main areas of cancer research in which ML methods are currently being applied, and discuss some of the advantages and disadvantages of their application. We also comment on and illustrate the integration of ML methods in clinical oncology decision support systems. 2012-03-28T15:19:41Z http://hdl.handle.net/2117/14742 Title: Artificial Intelligence tools applied to wastewater treatment Authors: Sánchez Marrè, Miquel; Cortés García, Claudio Ulises Abstract: The selection of an appropriate technique for the supervision and control of complex processes is crucial for achieving optimal results. A lot of effort, therefore, has been devoted to developing more efficient methodologies 2012-01-23T14:04:53Z http://hdl.handle.net/2117/14460 Title: Tracking deformable objects and dealing with same class object occlusion Authors: Alquézar Mancho, René; Amézquita, N; Serratosa Casanelles, Francesc Abstract: This paper presents an extension of a previously reported method for object tracking in video sequences to handle the problems of object crossing and occlusion by other objects in the same class that the one followed. The proposed solution is embedded in a system that integrates recognition and tracking in a probabilistic framework. In a recent work, a method to approach the object occlusion problem was proposed that failed when the object crossed or was occluded by another object of the same class. Here we present an attempt to overcome this limitation and show some promising results. The method is based on the assumption that when two objects cross each other there is not a brusque change of the trajectories. Our system uses object recognition results provided by a neural net that are computed from colour features of image regions for each frame. The location of tracked objects is represented through probability images that are updated dynamically using both recognition and tracking results. From these probabilities and a prediction of the motion of the object in the image, a binary decision is made for each pixel and object. 2012-01-10T19:22:24Z