Automatic learning for the classification of chemical reactions and in statistical thermodynamics

This Thesis describes the application of automatic learning methods for a) the classification of organic and metabolic reactions, and b) the mapping of Potential Energy Surfaces(PES). The classification of reactions was approached with two distinct methodologies: a representation of chemical reactions based on NMR data, and a representation of chemical reactions from the reaction equation based on the physico-chemical and topological features of chemical bonds. NMR-based classification of photochemical and enzymatic reactions. Photochemical and metabolic reactions were classified by Kohonen Self-Organizing Maps (Kohonen SOMs) and Random Forests (RFs) taking as input the difference between the 1H NMR spectra of the products and the reactants. The development of such a representation can be applied in automatic analysis of changes in the 1H NMR spectrum of a mixture and their interpretation in terms of the chemical reactions taking place. Examples of possible applications are the monitoring of reaction processes, evaluation of the stability of chemicals, or even the interpretation of metabonomic data. A Kohonen SOM trained with a data set of metabolic reactions catalysed by transferases was able to correctly classify 75% of an independent test set in terms of the EC number subclass. Random Forests improved the correct predictions to 79%. With photochemical reactions classified into 7 groups, an independent test set was classified with 86-93% accuracy. The data set of photochemical reactions was also used to simulate mixtures with two reactions occurring simultaneously. Kohonen SOMs and Feed-Forward Neural Networks (FFNNs) were trained to classify the reactions occurring in a mixture based on the 1H NMR spectra of the products and reactants. Kohonen SOMs allowed the correct assignment of 53-63% of the mixtures (in a test set). Counter-Propagation Neural Networks (CPNNs) gave origin to similar results. The use of supervised learning techniques allowed an improvement in the results. They were improved to 77% of correct assignments when an ensemble of ten FFNNs were used and to 80% when Random Forests were used. This study was performed with NMR data simulated from the molecular structure by the SPINUS program. In the design of one test set, simulated data was combined with experimental data. The results support the proposal of linking databases of chemical reactions to experimental or simulated NMR data for automatic classification of reactions and mixtures of reactions. Genome-scale classification of enzymatic reactions from their reaction equation. The MOLMAP descriptor relies on a Kohonen SOM that defines types of bonds on the basis of their physico-chemical and topological properties. The MOLMAP descriptor of a molecule represents the types of bonds available in that molecule. The MOLMAP descriptor of a reaction is defined as the difference between the MOLMAPs of the products and the reactants, and numerically encodes the pattern of bonds that are broken, changed, and made during a chemical reaction. The automatic perception of chemical similarities between metabolic reactions is required for a variety of applications ranging from the computer validation of classification systems, genome-scale reconstruction (or comparison) of metabolic pathways, to the classification of enzymatic mechanisms. Catalytic functions of proteins are generally described by the EC numbers that are simultaneously employed as identifiers of reactions, enzymes, and enzyme genes, thus linking metabolic and genomic information. Different methods should be available to automatically compare metabolic reactions and for the automatic assignment of EC numbers to reactions still not officially classified. In this study, the genome-scale data set of enzymatic reactions available in the KEGG database was encoded by the MOLMAP descriptors, and was submitted to Kohonen SOMs to compare the resulting map with the official EC number classification, to explore the possibility of predicting EC numbers from the reaction equation, and to assess the internal consistency of the EC classification at the class level. A general agreement with the EC classification was observed, i.e. a relationship between the similarity of MOLMAPs and the similarity of EC numbers. At the same time, MOLMAPs were able to discriminate between EC sub-subclasses. EC numbers could be assigned at the class, subclass, and sub-subclass levels with accuracies up to 92%, 80%, and 70% for independent test sets. The correspondence between chemical similarity of metabolic reactions and their MOLMAP descriptors was applied to the identification of a number of reactions mapped into the same neuron but belonging to different EC classes, which demonstrated the ability of the MOLMAP/SOM approach to verify the internal consistency of classifications in databases of metabolic reactions. RFs were also used to assign the four levels of the EC hierarchy from the reaction equation. EC numbers were correctly assigned in 95%, 90%, 85% and 86% of the cases (for independent test sets) at the class, subclass, sub-subclass and full EC number level,respectively. Experiments for the classification of reactions from the main reactants and products were performed with RFs - EC numbers were assigned at the class, subclass and sub-subclass level with accuracies of 78%, 74% and 63%, respectively. In the course of the experiments with metabolic reactions we suggested that the MOLMAP / SOM concept could be extended to the representation of other levels of metabolic information such as metabolic pathways. Following the MOLMAP idea, the pattern of neurons activated by the reactions of a metabolic pathway is a representation of the reactions involved in that pathway - a descriptor of the metabolic pathway. This reasoning enabled the comparison of different pathways, the automatic classification of pathways, and a classification of organisms based on their biochemical machinery. The three levels of classification (from bonds to metabolic pathways) allowed to map and perceive chemical similarities between metabolic pathways even for pathways of different types of metabolism and pathways that do not share similarities in terms of EC numbers. Mapping of PES by neural networks (NNs). In a first series of experiments, ensembles of Feed-Forward NNs (EnsFFNNs) and Associative Neural Networks (ASNNs) were trained to reproduce PES represented by the Lennard-Jones (LJ) analytical potential function. The accuracy of the method was assessed by comparing the results of molecular dynamics simulations (thermal, structural, and dynamic properties) obtained from the NNs-PES and from the LJ function. The results indicated that for LJ-type potentials, NNs can be trained to generate accurate PES to be used in molecular simulations. EnsFFNNs and ASNNs gave better results than single FFNNs. A remarkable ability of the NNs models to interpolate between distant curves and accurately reproduce potentials to be used in molecular simulations is shown. The purpose of the first study was to systematically analyse the accuracy of different NNs. Our main motivation, however, is reflected in the next study: the mapping of multidimensional PES by NNs to simulate, by Molecular Dynamics or Monte Carlo, the adsorption and self-assembly of solvated organic molecules on noble-metal electrodes. Indeed, for such complex and heterogeneous systems the development of suitable analytical functions that fit quantum mechanical interaction energies is a non-trivial or even impossible task. The data consisted of energy values, from Density Functional Theory (DFT) calculations, at different distances, for several molecular orientations and three electrode adsorption sites. The results indicate that NNs require a data set large enough to cover well the diversity of possible interaction sites, distances, and orientations. NNs trained with such data sets can perform equally well or even better than analytical functions. Therefore, they can be used in molecular simulations, particularly for the ethanol/Au (111) interface which is the case studied in the present Thesis. Once properly trained, the networks are able to produce, as output, any required number of energy points for accurate interpolations.

Tecnologia Java na representação de curvas e superfícies paramétricas

Dissertação apresentada na Faculdade de Ciências e Tecnologia da Universidade Nova de Lisboa para obtenção do grau de Mestre em Engenharia Informática

A Logic and tool for local reasoning about security protocols

Trabalho apresentado no âmbito do Mestrado em Engenharia Informática, como requisito parcial para obtenção do grau de Mestre em Engenharia Informática

Factorization by invariant embedding of elliptic problems: circular and star-shaped domains

Dissertação apresentada para obtenção do grau de Doutor em Matemática na especialidade de Equações Diferenciais, pela Universidade Nova de Lisboa,Faculdade de Ciências e Tecnologia

Simulation of the velocity field in compound channel flow using different closure models

1st European IAHR Congress,6-4 May, Edinburg, Scotland

Desigualdades socioeconómicas na doença cardiovascular em Portugal : estudo baseado no 4º Inquérito Nacional de Saúde

RESUMO - Em Portugal, as doenças cardiovasculares (DCV), incluindo o acidente vascular cerebral (AVC) e a doença cardíaca isquémica (DCI), são das principais causas de morbi-mortalidade e invalidez. Sabe-se que o nível socioeconómico (NSE) influencia o estado de saúde, todavia são escassas as evidências sobre as desigualdades socioeconómicas na DCV em Portugal. O objectivo deste estudo foi analisar a distribuição da DCV de acordo com o NSE na população portuguesa. Foi realizado um estudo transversal exploratório-descritivo usando a base de dados do 4º Inquérito Nacional de Saúde, 2005/06. As desigualdades socioeconómicas nas DCV, AVC e DCI, factores de risco [sedentarismo, hipertensão arterial (HTA), diabetes mellitus (DM), tabagismo, obesidade e sofrimento psicológico (Mental Health Inventory ≤ 52)] e número de consultas médicas, foram analisadas através dos odds ratio por NSE (rendimento familiar equivalente, escala modificada da OCDE) com intervalo de confiança de 95% e dos índices e curvas de concentração. Dos 21 807 indivíduos, 53,34% são do sexo feminino, a idade média é de 54±11 e entre 35 e 74 anos. A DCV, a DCI, o AVC, a HTA, a DM e a obesidade estão associados com NSE mais baixos, o tabagismo está associado aos NSE mais elevados, enquanto o sedentarismo, o número de consultas médicas e o sofrimento psicológico não apresentam associação significativa com o NSE. Os resultados revelam a associação entre os estilos de vida, morbilidade e NSE e demonstram que são necessárias políticas de saúde mais abrangentes, de acordo com as características individuais, culturais e socioeconómicas e dirigidas à promoção da saúde e prevenção da doença. -------------------------------------------- ABSTRACT - Cardiovascular diseases (CVD), including stroke and ischemic heart disease (IHD), are the leading causes of morbidity, mortality and disability in Portugal. It is known that socioeconomic status (SES) influences health status; however there is little evidence about socioeconomic inequalities in CVD in Portugal. The aim of this study was to analyze the distribution of CVD according to SES in the Portuguese population. We conducted a cross-sectional descriptive exploratory study using the database of the 4th National Survey of Health, 2005/06. Socioeconomic inequalities in CVD, stroke, IHD, risk factors [physical inactivity, arterial hypertension (AHT), diabetes mellitus (DM), smoking, obesity and psychological distress (Mental Health Inventory ≤ 52)], as well as the number of medical visits, were analyzed by SES (family income using the OECD modified equivalent scale) using odds ratio (confidence interval = 95%), and concentration curves and indices. Of the 21 807 individuals, 53.34% are female, aged between 35 and 74 with mean 54 ± 11 years. CVD, IHD, stroke, AHT, MD and obesity are associated with lower SES, smoking is associated with higher SES, while physical inactivity, number of medical visits and psychological distress showed no significant association with SES. Results suggest an association between lifestyle, morbidity and SES. They also demonstrate the need for comprehensive health strategies, involving health promotion and disease prevention, that incorporate individual, cultural and socioeconomic characteristics.

Gold nanoparticle-DNA conjugates for oligonucleotide vectorization towards gene silencing

Dissertação para obtenção do Grau de Mestre em Genética Molecular e Biomedicina