Computational methods for prediction of T-cell epitopes - a framework for modelling, testing, and applications

Computational models complement laboratory experimentation for efficient identification of MHC-binding peptides and T-cell epitopes. Methods for prediction of MHC-binding peptides include binding motifs, quantitative matrices, artificial neural networks, hidden Markov models, and molecular modelling. Models derived by these methods have been successfully used for prediction of T-cell epitopes in cancer, autoimmunity, infectious disease, and allergy. For maximum benefit, the use of computer models must be treated as experiments analogous to standard laboratory procedures and performed according to strict standards. This requires careful selection of data for model building, and adequate testing and validation. A range of web-based databases and MHC-binding prediction programs are available. Although some available prediction programs for particular MHC alleles have reasonable accuracy, there is no guarantee that all models produce good quality predictions. In this article, we present and discuss a framework for modelling, testing, and applications of computational methods used in predictions of T-cell epitopes. (C) 2004 Elsevier Inc. All rights reserved.

Virtual simulation of the postsurgical cosmetic outcome in patientswith pectus excavatum

Pectus excavatum is the most common congenital deformity of the anterior chest wall, in which several ribs and the sternum grow abnormally. Nowadays, the surgical correction is carried out in children and adults through Nuss technic. This technic has been shown to be safe with major drivers as cosmesis and the prevention of psychological problems and social stress. Nowadays, no application is known to predict the cosmetic outcome of the pectus excavatum surgical correction. Such tool could be used to help the surgeon and the patient in the moment of deciding the need for surgery correction. This work is a first step to predict postsurgical outcome in pectus excavatum surgery correction. Facing this goal, it was firstly determined a point cloud of the skin surface along the thoracic wall using Computed Tomography (before surgical correction) and the Polhemus FastSCAN (after the surgical correction). Then, a surface mesh was reconstructed from the two point clouds using a Radial Basis Function algorithm for further affine registration between the meshes. After registration, one studied the surgical correction influence area (SCIA) of the thoracic wall. This SCIA was used to train, test and validate artificial neural networks in order to predict the surgical outcome of pectus excavatum correction and to determine the degree of convergence of SCIA in different patients. Often, ANN did not converge to a satisfactory solution (each patient had its own deformity characteristics), thus invalidating the creation of a mathematical model capable of estimating, with satisfactory results, the postsurgical outcome

Sistema de apoio à decisão para a classificação de risco em projectos de tecnologias de informação

O trabalho que a seguir se apresenta tem como objectivo descrever a criação de um modelo que sirva de suporte a um sistema de apoio à decisão sobre o risco inerente à execução de projectos na área das Tecnologias de Informação (TI) recorrendo a técnicas de mineração de dados. Durante o ciclo de vida de um projecto, existem inúmeros factores que contribuem para o seu sucesso ou insucesso. A responsabilidade de monitorizar, antever e mitigar esses factores recai sobre o Gestor de Projecto. A gestão de projectos é uma tarefa difícil e dispendiosa, consome muitos recursos, depende de numerosas variáveis e, muitas vezes, até da própria experiência do Gestor de Projecto. Ao ser confrontado com as previsões de duração e de esforço para a execução de uma determinada tarefa, o Gestor de Projecto, exceptuando a sua percepção e intuição pessoal, não tem um modo objectivo de medir a plausibilidade dos valores que lhe são apresentados pelo eventual executor da tarefa. As referidas previsões são fundamentais para a organização, pois sobre elas são tomadas as decisões de planeamento global estratégico corporativo, de execução, de adiamento, de cancelamento, de adjudicação, de renegociação de âmbito, de adjudicação externa, entre outros. Esta propensão para o desvio, quando detectada numa fase inicial, pode ajudar a gerir melhor o risco associado à Gestão de Projectos. O sucesso de cada projecto terminado foi qualificado tendo em conta a ponderação de três factores: o desvio ao orçamentado, o desvio ao planeado e o desvio ao especificado. Analisando os projectos decorridos, e correlacionando alguns dos seus atributos com o seu grau de sucesso o modelo classifica, qualitativamente, um novo projecto quanto ao seu risco. Neste contexto o risco representa o grau de afastamento do projecto ao sucesso. Recorrendo a algoritmos de mineração de dados, tais como, árvores de classificação e redes neuronais, descreve-se o desenvolvimento de um modelo que suporta um sistema de apoio à decisão baseado na classificação de novos projectos. Os modelos são o resultado de um extensivo conjunto de testes de validação onde se procuram e refinam os indicadores que melhor caracterizam os atributos de um projecto e que mais influenciam o risco. Como suporte tecnológico para o desenvolvimento e teste foi utilizada a ferramenta Weka 3. Uma boa utilização do modelo proposto possibilitará a criação de planos de contingência mais detalhados e uma gestão mais próxima para projectos que apresentem uma maior propensão para o risco. Assim, o resultado final pretende constituir mais uma ferramenta à disposição do Gestor de Projecto.

Controlo tolerante a falhas de um canal de rega

A presente dissertação pretende conceber e implementar um sistema de controlo tolerante a falhas, no canal experimental de rega da Universidade de Évora, utilizando um modelo implementado em MATLAB/SIMULINK®. Como forma de responder a este desafio, analisaram-se várias técnicas de diagnóstico de falhas, tendo-se optado por técnicas baseadas em redes neuronais para o desenvolvimento de um sistema de detecção e isolamento de falhas no canal de rega, sem ter em conta o tipo de sistema de controlo utilizado. As redes neuronais foram, assim, os processadores não lineares utilizados e mais aconselhados em situações onde exista uma abundância de dados do processo, porque aprendem por exemplos e são suportadas por teorias estatísticas e de optimização, focando não somente o processamento de sinais, como também expandindo os horizontes desse processamento. A ênfase dos modelos das redes neuronais está na sua dinâmica, na sua estabilidade e no seu comportamento. Portanto, o trabalho de investigação do qual resultou esta Dissertação teve como principais objectivos o desenvolvimento de modelos de redes neuronais que representassem da melhor forma a dinâmica do canal de rega, de modo a obter um sistema de detecção de falhas que faça uma comparação entre os valores obtidos nos modelos e no processo. Com esta diferença de valores, da qual resultará um resíduo, é possível desenvolver tanto o sistema de detecção como de isolamento de falhas baseados nas redes neuronais, possibilitando assim o desenvolvimento dum sistema de controlo tolerante a falhas, que engloba os módulos de detecção, de isolamento/diagnóstico e de reconfiguração do canal de rega. Em síntese, na Dissertação realizada desenvolveu-se um sistema que permite reconfigurar o processo em caso de ocorrência de falhas, melhorando significativamente o desempenho do canal de rega.

An artificial neural network approach for short-term wind power forecasting in Portugal

This paper presents an artificial neural network approach for short-term wind power forecasting in Portugal. The increased integration of wind power into the electric grid, as nowadays occurs in Portugal, poses new challenges due to its intermittency and volatility. Hence, good forecasting tools play a key role in tackling these challenges. The accuracy of the wind power forecasting attained with the proposed approach is evaluated against persistence and ARIMA approaches, reporting the numerical results from a real-world case study.

Dynamic artificial neural network for electricity market prices forecast

This paper presents an artificial neural network applied to the forecasting of electricity market prices, with the special feature of being dynamic. The dynamism is verified at two different levels. The first level is characterized as a re-training of the network in every iteration, so that the artificial neural network can able to consider the most recent data at all times, and constantly adapt itself to the most recent happenings. The second level considers the adaptation of the neural network’s execution time depending on the circumstances of its use. The execution time adaptation is performed through the automatic adjustment of the amount of data considered for training the network. This is an advantageous and indispensable feature for this neural network’s integration in ALBidS (Adaptive Learning strategic Bidding System), a multi-agent system that has the purpose of providing decision support to the market negotiating players of MASCEM (Multi-Agent Simulator of Competitive Electricity Markets).

Previsão de vento baseado em técnicas de Data Mining

Mestrado em Engenharia Electrotécnica – Sistemas Eléctricos de Energia

Mixed integer non linear programming and artificial neural network based approach to ancillary services dispatch in competitive electricity markets

Ancillary services represent a good business opportunity that must be considered by market players. This paper presents a new methodology for ancillary services market dispatch. The method considers the bids submitted to the market and includes a market clearing mechanism based on deterministic optimization. An Artificial Neural Network is used for day-ahead prediction of Regulation Down, regulation-up, Spin Reserve and Non-Spin Reserve requirements. Two test cases based on California Independent System Operator data concerning dispatch of Regulation Down, Regulation Up, Spin Reserve and Non-Spin Reserve services are included in this paper to illustrate the application of the proposed method: (1) dispatch considering simple bids; (2) dispatch considering complex bids.

Mining protein structure data

The principal topic of this work is the application of data mining techniques, in particular of machine learning, to the discovery of knowledge in a protein database. In the first chapter a general background is presented. Namely, in section 1.1 we overview the methodology of a Data Mining project and its main algorithms. In section 1.2 an introduction to the proteins and its supporting file formats is outlined. This chapter is concluded with section 1.3 which defines that main problem we pretend to address with this work: determine if an amino acid is exposed or buried in a protein, in a discrete way (i.e.: not continuous), for five exposition levels: 2%, 10%, 20%, 25% and 30%. In the second chapter, following closely the CRISP-DM methodology, whole the process of construction the database that supported this work is presented. Namely, it is described the process of loading data from the Protein Data Bank, DSSP and SCOP. Then an initial data exploration is performed and a simple prediction model (baseline) of the relative solvent accessibility of an amino acid is introduced. It is also introduced the Data Mining Table Creator, a program developed to produce the data mining tables required for this problem. In the third chapter the results obtained are analyzed with statistical significance tests. Initially the several used classifiers (Neural Networks, C5.0, CART and Chaid) are compared and it is concluded that C5.0 is the most suitable for the problem at stake. It is also compared the influence of parameters like the amino acid information level, the amino acid window size and the SCOP class type in the accuracy of the predictive models. The fourth chapter starts with a brief revision of the literature about amino acid relative solvent accessibility. Then, we overview the main results achieved and finally discuss about possible future work. The fifth and last chapter consists of appendices. Appendix A has the schema of the database that supported this thesis. Appendix B has a set of tables with additional information. Appendix C describes the software provided in the DVD accompanying this thesis that allows the reconstruction of the present work.

Estimação de cobertura rádio em GSM-R através de redes neuronais

Trabalho Final de Mestrado para obtenção do grau de Mestre em Engenharia de Eletrónica e Telecomunicações

Aplicação de redes neuronais artificiais à deteção e isolamento de falhas em processos industriais

Trabalho Final de Mestrado para obtenção do grau de Mestre em Engenharia Mecânica

Complex order biped rhythms

Animal locomotion is a complex process, involving the central pattern generators (neural networks, located in the spinal cord, that produce rhythmic patterns), the brainstem command systems, the steering and posture control systems and the top layer structures that decide which motor primitive is activated at a given time. Pinto and Golubitsky studied an integer CPG model for legs rhythms in bipeds. It is a four-coupled identical oscillators' network with dihedral symmetry. This paper considers a new complex order central pattern generator (CPG) model for locomotion in bipeds. A complex derivative Dα±jβ, with α, β ∈ ℜ+, j = √-1, is a generalization of the concept of an integer derivative, where α = 1, β = 0. Parameter regions where periodic solutions, identified with legs' rhythms in bipeds, occur, are analyzed. Also observed is the variation of the amplitude and period of periodic solutions with the complex order derivative.

Desenvolvimento do padrão de crescimento urbano: modelação da dinâmica do solo urbano em Andaluzia

Dissertação apresentada como requisito parcial para obtenção do grau de Mestre em Ciência e Sistemas de Informação Geográfica

Topological entropy of catalytic sets: Hypercycles revisited

The dynamics of catalytic networks have been widely studied over the last decades because of their implications in several fields like prebiotic evolution, virology, neural networks, immunology or ecology. One of the most studied mathematical bodies for catalytic networks was initially formulated in the context of prebiotic evolution, by means of the hypercycle theory. The hypercycle is a set of self-replicating species able to catalyze other replicator species within a cyclic architecture. Hypercyclic organization might arise from a quasispecies as a way to increase the informational containt surpassing the so-called error threshold. The catalytic coupling between replicators makes all the species to behave like a single and coherent evolutionary multimolecular unit. The inherent nonlinearities of catalytic interactions are responsible for the emergence of several types of dynamics, among them, chaos. In this article we begin with a brief review of the hypercycle theory focusing on its evolutionary implications as well as on different dynamics associated to different types of small catalytic networks. Then we study the properties of chaotic hypercycles with error-prone replication with symbolic dynamics theory, characterizing, by means of the theory of topological Markov chains, the topological entropy and the periods of the orbits of unimodal-like iterated maps obtained from the strange attractor. We will focus our study on some key parameters responsible for the structure of the catalytic network: mutation rates, autocatalytic and cross-catalytic interactions.

Neural correlations during brain activation in arithmetical tasks – an approach using electroencephalographic data

Dissertação apresentada na Faculdade de Ciências e Tecnologiea da Universidade Nova de Lisboa, para obtenção do Grau de Mestre em Engenharia Biomédica