MicroRNAs (miRNAs) são pequenos RNAs não codificadores de proteínas que atuam principalmente como silenciadores pós-transcricionais, inibindo a tradução de RNAs mensageiros. Evidências crescentes revelam que tais moléculas desempenham papéis críticos em muitos processos biológicos importantes. Uma vez que não existem anotações de termos de miRNAs na Gene Ontology (GO), tampouco um banco de dados de referência com anotações funcionais dos mesmos, o cálculo da medida de similaridade entre miRNAs de forma direta não possui um padrão estabelecido. Por outro lado, a existência de bancos de dados de genes-alvo de miRNAs, como o TarBase, e bases de dados contendo informações sobre associações de miRNAs e doenças humanas, como o HMDD, nos permite inferir a similaridade funcional dos miRNAs indiretamente, por meio da análise de seus genes-alvo na GO ou entre suas doenças relacionadas na ontologia MeSH. Além disso, de acordo com a estrutura da ontologia de miRNAs OMIT, um miRNA também pode ser anotado com outras informações, tais como a sua natureza de atuação como oncogênico ou supressor de tumor, o organismo em que se encontra, o tipo de experimento em que foi encontrado, suas associações com doenças, genes-alvo, proteínas e eventos patológicos. Dessa forma, a similaridade entre miRNAs pode ser inferida com base na combinação de um conjunto de informações contidas nas respectivas anotações, de forma que possamos obter um aproveitamento de várias informações existentes, definindo assim um cálculo de similaridade funcional composta. Assim, neste trabalho, propomos a criação e aplicação de um método chamado CFSim, aplicado sobre a OMIT e que utiliza a ontologia de doenças, MeSH, e a ontologia de genes, GO, para calcular a similaridade entre dois miRNAs, juntamente com informações contidas em suas anotações. A validação de nosso método foi realizada por meio da comparação com a similaridade funcional inferida considerando diferentes famílias de miRNAs e os resultados obtidos mostraram que nosso método é eficiente, no sentido de que a similaridade entre miRNAs pertencentes à mesma família é maior que a similaridade entre miRNAs de famílias distintas. Ainda, em comparação com os métodos de similaridade funcional já existentes na literatura, o CFSim obteve melhores resultados. Adicionalmente, para tornarmos viável a utilização do método proposto, foi projetado e implementado um ambiente contendo a infraestrutura necessária para que pesquisadores possam incluir dados obtidos de novas descobertas e consultar as informações sobre um determinado miRNA, assim como calcular a similaridade entre dois miRNAs, baseada no método proposto. / MicroRNAs (miRNAs) are small non-coding RNA that mainly negatively regulate gene expression by inhibiting translation of target RNAs. Increasing evidences show that such molecules play critical roles in many important biological processes. Since there are no terms of miRNAs annotations in Gene Ontology (GO), nor a database with microRNAs functional annotations, directly calculating the functional similarity between miRNAs does not have an estabilished pattern aproach. However, the existence of miRNAs target genes database, such as TarBase, and a miRNAs-disease associations database, such as HMDD, allow us to indirectly infer functional similarity of miRNAs through the analysis of their target genes in GO or between their related diseases in MeSH. Moreover, according to the structure of the ontology of miRNAs OMIT, a miRNA can also be annotated with other information, such as if it acts as an oncogene or a tumor suppressor, the organism that it belongs, the experiment in which it was found, its associations with diseases, target genes, proteins and pathological events. Thus, miRNAs similarity can be inferred based on the combination of a broad set of information contained in their annotations, indeed, we can use all available information defining the calculation of a composed functional similarity. In this study, we propose the creation and application of CFSim method applied to the OMIT using the diseases ontology, MeSH, and gene ontology, GO, to compute miRNAs similarity based on different information in their annotations. We validated our method by comparing with functional similarity inferred by miRNA families and the results showed that our method is efficient in sense that the functional similarity between miRNAs in the same family was greater compared to other miRNAs from distinct families. Furthermore, in comparison with existing methods of functional similarity in the literature until the present day, the CFSim showed better results. Finally, to make feasible the use of the proposed method, an environment was designed and implemented, containing the necessary infrastructure so that researchers can include data from new discoveries and see information about a particular miRNA, as well as calculate the similarity between two miRNAs, based in the proposed method.

Software project managers usually use benchmarking repositories to estimate effort, cost, and duration of the software development which will be used to appropriately plan, monitor and control the project activities. In addition, precision of benchmarking repositories is a critical factor in software effort estimation process which plays subsequently a critical role in the success of the software development project. In order to construct such a precise benchmarking data repository, it is important to have defined benchmarking data attributes and data characteristics and to have collected project data accordingly. On the other hand, studies show that data characteristics of benchmark data sets have impact on generalizing the studies which are based on using these datasets. Quality of data repository is not only depended on quality of collected data, but also it is related to how these data are collected. In this thesis, a benchmarking methodology is proposed for organizations to collect benchmarking data for effort estimation purposes. This methodology consists of three main components: benchmarking measures, benchmarking data collection processes, and benchmarking data collection tool. In this approach results of previous studies from the literature were used too. In order to verify and validate the methodology project data were collected in two middle size software organizations and one small size organization by using automated benchmarking data collection tool. Also, effort estimation models were constructed and evaluated for these projects data and impact of different characteristics of the projects was inspected in effort estimation models.

Simultaneous measurement of the expression levels of thousands to ten thousand genes in multiple tissue types is a result of advancement in microarray technology. These expression levels provide clues about the gene functions and that have enabled better diagnosis and treatment of serious disease like cancer. To solve the mystery of unknown gene functions, biological to statistical mapping is needed in terms of classifying the genes. Here we introduce a novel approach of combining both statistical consistency and biological relevance of the clusters produced by a clustering method. Here we employ two performance measures in combination for measuring statistical stability and functional similarity of the cluster members using a set of gene expressions with known biological functions. Through this analysis we construct a platform to predict about unknown gene functions using the outperforming clustering algorithm.

Simultaneous measurement of the expression levels of thousands to ten thousand genes in multiple tissue types is a result of advancement in microarray technology. These expression levels provide clues about the gene functions and that have enabled better diagnosis and treatment of serious disease like cancer. To solve the mystery of unknown gene functions, biological to statistical mapping is needed in terms of classifying the genes. Here we introduce a novel approach of combining both statistical consistency and biological relevance of the clusters produced by a clustering method. Here we employ two performance measures in combination for measuring statistical stability and functional similarity of the cluster members using a set of gene expressions with known biological functions. Through this analysis we construct a platform to predict about unknown gene functions using the outperforming clustering algorithm.

MicroRNAs (miRNAs) são pequenos RNAs não codificadores de proteínas que atuam principalmente como silenciadores pós-transcricionais, inibindo a tradução de RNAs mensageiros. Evidências crescentes revelam que tais moléculas desempenham papéis críticos em muitos processos biológicos importantes. Uma vez que não existem anotações de termos de miRNAs na Gene Ontology (GO), tampouco um banco de dados de referência com anotações funcionais dos mesmos, o cálculo da medida de similaridade entre miRNAs de forma direta não possui um padrão estabelecido. Por outro lado, a existência de bancos de dados de genes-alvo de miRNAs, como o TarBase, e bases de dados contendo informações sobre associações de miRNAs e doenças humanas, como o HMDD, nos permite inferir a similaridade funcional dos miRNAs indiretamente, por meio da análise de seus genes-alvo na GO ou entre suas doenças relacionadas na ontologia MeSH. Além disso, de acordo com a estrutura da ontologia de miRNAs OMIT, um miRNA também pode ser anotado com outras informações, tais como a sua natureza de atuação como oncogênico ou supressor de tumor, o organismo em que se encontra, o tipo de experimento em que foi encontrado, suas associações com doenças, genes-alvo, proteínas e eventos patológicos. Dessa forma, a similaridade entre miRNAs pode ser inferida com base na combinação de um conjunto de informações contidas nas respectivas anotações, de forma que possamos obter um aproveitamento de várias informações existentes, definindo assim um cálculo de similaridade funcional composta. Assim, neste trabalho, propomos a criação e aplicação de um método chamado CFSim, aplicado sobre a OMIT e que utiliza a ontologia de doenças, MeSH, e a ontologia de genes, GO, para calcular a similaridade entre dois miRNAs, juntamente com informações contidas em suas anotações. A validação de nosso método foi realizada por meio da comparação com a similaridade funcional inferida considerando diferentes famílias de miRNAs e os resultados obtidos mostraram que nosso método é eficiente, no sentido de que a similaridade entre miRNAs pertencentes à mesma família é maior que a similaridade entre miRNAs de famílias distintas. Ainda, em comparação com os métodos de similaridade funcional já existentes na literatura, o CFSim obteve melhores resultados. Adicionalmente, para tornarmos viável a utilização do método proposto, foi projetado e implementado um ambiente contendo a infraestrutura necessária para que pesquisadores possam incluir dados obtidos de novas descobertas e consultar as informações sobre um determinado miRNA, assim como calcular a similaridade entre dois miRNAs, baseada no método proposto. / MicroRNAs (miRNAs) are small non-coding RNA that mainly negatively regulate gene expression by inhibiting translation of target RNAs. Increasing evidences show that such molecules play critical roles in many important biological processes. Since there are no terms of miRNAs annotations in Gene Ontology (GO), nor a database with microRNAs functional annotations, directly calculating the functional similarity between miRNAs does not have an estabilished pattern aproach. However, the existence of miRNAs target genes database, such as TarBase, and a miRNAs-disease associations database, such as HMDD, allow us to indirectly infer functional similarity of miRNAs through the analysis of their target genes in GO or between their related diseases in MeSH. Moreover, according to the structure of the ontology of miRNAs OMIT, a miRNA can also be annotated with other information, such as if it acts as an oncogene or a tumor suppressor, the organism that it belongs, the experiment in which it was found, its associations with diseases, target genes, proteins and pathological events. Thus, miRNAs similarity can be inferred based on the combination of a broad set of information contained in their annotations, indeed, we can use all available information defining the calculation of a composed functional similarity. In this study, we propose the creation and application of CFSim method applied to the OMIT using the diseases ontology, MeSH, and gene ontology, GO, to compute miRNAs similarity based on different information in their annotations. We validated our method by comparing with functional similarity inferred by miRNA families and the results showed that our method is efficient in sense that the functional similarity between miRNAs in the same family was greater compared to other miRNAs from distinct families. Furthermore, in comparison with existing methods of functional similarity in the literature until the present day, the CFSim showed better results. Finally, to make feasible the use of the proposed method, an environment was designed and implemented, containing the necessary infrastructure so that researchers can include data from new discoveries and see information about a particular miRNA, as well as calculate the similarity between two miRNAs, based in the proposed method.

In this study, we identified one of the reasons of the low correlation between functional size and development effort which is overlooking the similarity of the functions during the mapping of the functional size and development effort. We developed a methodology (SiRFuS) that is based on the idea of the reuse of the similar functions internally to provide high correlation between functional size and development effort. The method is developed for the identification of the similar functions based on the method of Santillo and Abran. Similarity percentages among the functional processes and Similarity Reflective Functional Sizes are computed to attain adjusted functional sizes. The similarity reflective functional sizes were named as Discrete Similarity Reflective Functional Size and Continuous Similarity Reflective Functional Size based on the characteristics of the adjusted functional sizes. The SiRFuS method consists of three stages: measurement of the software product with COSMIC Functional Size Measurement (FSM) method / identification of the functional similarities bases on the measurement results and calculation of the similarity reflective functional sizes. In order to facilitate the detection of similar functions, calculation of the percentage of the similarities and similarity reflective functional sizes / a software tool is developed based on the SiRFuS method. Two case studies were performed in order to identify the improvement opportunities and evaluate the applicability of the method and the tool.

The estimation of effort is at the heart of project tasks, since it is used for many purposes such as cost estimation, budgeting, monitoring, project planning, control and software investments. Researchers analyze problems of the estimation, propose new models and use new techniques to improve accuracy. However up to now, there is no comprehensive estimation methodology to guide companies in their effort estimation tasks. Effort estimation problem is not only a computational but also a managerial problem. It requires estimation goals, execution steps, applied measurement methods and updating mechanisms to be properly defined. Besides project teams should have motivation and responsibilities to build a reliable database. If such methodology is not defined, common interpretation will not be constituted among software teams of the company, and variances in measurements and divergences in collected information prevents to collect sufficient historical information for building accurate models. This thesis proposes a methodology for organizations to manage and execute effort estimation processes. The approach is based on the reported best practices, v empirical results of previous studies and solutions to problems &amp / conflicts described in literature. Five integrated processes: Data Collection, Size Measurement, Data Analysis, Calibration, Effort Estimation processes are developed with their artifacts, procedures, checklists and templates. The validation and applicability of the methodology is checked in a middle-size software company. During the validation of methodology we also evaluated some concepts such as Functional Similarity (FS) and usage of Base Functional Components (BFC) in effort model on a reliable dataset. By this way we evaluated whether these subjects should be a part of methodology or not. Besides in this study it is the first time that the COSMIC has been used for Artificial Neural Network models.

L’objectif de ce travail de thèse était d’approfondir les connaissances actuelles sur l’organisation des concepts d’objets fabriqués manipulables. Plus particulièrement, nous nous sommes intéressés au traitement implicite des relations thématiques (e.g., scie-bois) et des relations de similarité fonctionnelle spécifique (e.g., scie-hâche) et générale (e.g., scie-couteau) lors de l’identification des objets fabriqués manipulables. Les stimuli ont été sélectionnés par une tâche de génération de propriétés et le traitement implicite des relations sémantiques a été évalué grâce à l’enregistrement des mouvements oculaires dans le Paradigme du Monde Visuel. Tout d’abord, nous avons évalué le développement du traitement implicite des relations thématiques et de similarité fonctionnelle chez les enfants de 6-, 8- 10- ans et chez l’adulte. Les résultats ont montré que le traitement implicite des relations de similarité fonctionnelle générale évolue progressivement avec l’âge, alors que les relations thématiques sont déjà implicitement traitées dès 6-ans. Ensuite, nous avons testé le rôle de l’action dans le traitement de ces relations. Chez l’adulte, les résultats ont montré que l’action amorce le traitement des relations thématiques différemment en fonction du niveau de représentation de l’action impliqué. Le traitement thématique est facilité par l’action représentée au niveau du geste mais gêné par l’action représentée au niveau de l’intention. Chez l’enfant, les données ont montré que le traitement de relations de similarité fonctionnelle générale est facilité par l’action représentée au niveau de l’intention. Les données préliminaires chez le patient cérébro-lésé renforcent l’idée d’une structure conceptuelle multidéterminée et graduelle. Dans l’ensemble, ce travail démontre que les connaissances sur les objets fabriqués manipulables sont organisées selon des relations sémantiques distinctes qui présentent des trajectoires développementales différentes et correspondent à diffèrent niveaux de représentation d’action. / The general aim of this work was to provide a better understanding of the cognitive mechanisms underlying manipulable artifact object conceptual organization. Specifically, we investigated implicit processing of thematic (e.g., saw-wood) and functional similarity relations at the specific (e.g., saw-axe) and general (e.g., saw-knife) levels during manipulable artifact object identification. Stimuli were selected from property generation and implicit semantic processing was investigated using eye-tracking in the Visual World Paradigm. First, we assessed the development of thematic and functional similarity processing in 6-, 8-, 10- year-old children and adults. Results demonstrated progressive emergence of general function similarity processing with age, while thematic and specific function similarity processing was already present from 6. Findings support a graded involvement of distinct mechanisms in object semantic processing and development. In the second series of experiments, we investigated the role of action in thematic and functional similarity processing by combining action priming with the Visual World Paradigm in adults and 6-year-olds. In adults, action primed thematic processing differently depending on the level of action representation entailed. Thematic processing was facilitated by gesture-level action representations but disturbed by intention-level action representations. In 6-year-olds, intention-level action representations improved general functional similarity relation implicit processing. Findings highlight the role of different action representation levels in manipulable artifact object semantic processing. Finally, preliminary data collected in 8 stroke patients provided additional evidence in favor of a multidetermined and graded manipulable artifact semantic structure. Together, findings demonstrate that knowledge about manipulable artifacts is organized along distinct types of semantic relations that show different developmental trajectories and relate to different levels of action representations.

L’objectif de ce travail est de mettre au point une nouvelle approche automatique pour identifier les réseaux de gènes concourant à une même fonction biologique. Ceci permet une meilleure compréhension des phénomènes biologiques et notamment des processus impliqués dans les maladies telles que les cancers. Différentes stratégies ont été développées pour essayer de regrouper les gènes d’un organisme selon leurs relations fonctionnelles : génétique classique et génétique moléculaire. Ici, nous utilisons une propriété connue des réseaux de gènes fonctionnellement liés à savoir que ces gènes sont généralement co-régulés et donc co-exprimés. Cette co-régulation peut être mise en évidence par des méta-analyses de données de puces à ADN (micro-arrays) telles que Gemma ou COXPRESdb. Dans un travail précédent [Al Adhami et al., 2015], la topologie d’un réseau de co-expression de gènes a été caractérisé en utilisant deux paramètres de description des réseaux qui discriminent des groupes de gènes sélectionnés aléatoirement (modules aléatoires, RM) de groupes de gènes avec des liens fonctionnels connus (modules fonctionnels, FM), c’est-à-dire des gènes appartenant au même processus biologique GO. Dans le présent travail, nous avons cherché à généraliser cette approche et à proposer une méthode, appelée TopoFunc, pour améliorer l’annotation existante de la fonction génique. Nous avons d’abord testé différents descripteurs topologiques du réseau de co-expression pour sélectionner ceux qui identifient le mieux des modules fonctionnels. Puis, nous avons constitué une base de données rassemblant des modules fonctionnels et aléatoires, pour lesquels, sur la base des descripteurs sélectionnés, nous avons construit un modèle de discrimination LDA [Friedman et al., 2001] permettant, pour un sous-ensemble de gènes donné, de prédire son type (fonctionnel ou non). Basée sur la méthode de similarité de gènes travaillée par Wang et ses collègues [Wang et al., 2007], nous avons calculé un score de similarité fonctionnelle entre les gènes d’un module. Nous avons combiné ce score avec celui du modèle LDA dans une fonction de fitness implémenté dans un algorithme génétique (GA). À partir du processus biologique d’ontologie de gènes donné (GO-BP), AG visait à éliminer les gènes faiblement co-exprimés avec la plus grande clique de GO-BP et à ajouter des gènes «améliorant» la topologie et la fonctionnalité du module. Nous avons testé TopoFunc sur 193 GO-BP murins comprenant 50-100 gènes et avons montré que TopoFunc avait agrégé un certain nombre de nouveaux gènes avec le GO-BP initial tout en améliorant la topologie des modules et la similarité fonctionnelle. Ces études peuvent être menées sur plusieurs espèces (homme, souris, rat, et possiblement poulet et poisson zèbre) afin d’identifier des modules fonctionnels conservés au cours de l’évolution. / The aim of this work is to develop a new automatic approach to identify networks of genes involved in the same biological function. This allows a better understanding of the biological phenomena and in particular of the processes involved in diseases such as cancers. Various strategies have been developed to try to cluster genes of an organism according to their functional relationships : classical genetics and molecular genetics. Here we use a well-known property of functionally related genes mainly that these genes are generally co-regulated and therefore co-expressed. This co-regulation can be detected by microarray meta-analyzes databases such as Gemma or COXPRESdb. In a previous work [Al Adhami et al., 2015], the topology of a gene coexpression network was characterized using two description parameters of networks that discriminate randomly selected groups of genes (random modules, RM) from groups of genes with known functional relationship (functional modules, FM), e.g. genes that belong to the same GO Biological Process. We first tested different topological descriptors of the co-expression network to select those that best identify functional modules. Then, we built a database of functional and random modules for which, based on the selected descriptors, we constructed a discrimination model (LDA)[Friedman et al., 2001] allowing, for a given subset of genes, predict its type (functional or not). Based on the similarity method of genes worked by Wang and co-workers [Wang et al., 2007], we calculated a functional similarity score between the genes of a module. We combined this score with that of the LDA model in a fitness function implemented in a genetic algorithm (GA). Starting from a given Gene Ontology Biological Process (GO-BP), AG aimed to eliminate genes that were weakly coexpressed with the largest clique of the GO-BP and to add genes that "improved" the topology and functionality of the module. We tested TopoFunc on the 193 murine GO-BPs comprising 50-100 genes and showed that TopoFunc aggregated a number of novel genes to the initial GO-BP while improving module topology and functional similarity. These studies can be conducted on several species (humans, mice, rats, and possibly chicken and zebrafish) to identify functional modules preserved during evolution.

<p>The present study investigates lexical similarity perceptions by students of Swedish as a foreign language (L3) with a good yet non-native proficiency in English (L2). The general theoretical framework is provided by studies in transfer of learning and its specific instance, transfer in language acquisition.</p><p>It is accepted as true that all previous linguistic knowledge is facilitative in developing proficiency in a new language. However, a frequently reported phenomenon is that students see similarities between two systems in a different way than linguists and theoreticians of education do. As a consequence, the full facilitative potential of transfer remains unused.</p><p>The present research seeks to shed light on the similarity perceptions with the focus on the comprehension of a written text. In order to elucidate students’ views, a form involving similarity judgements and multiple choice questions for formally similar items has been designed, drawing on real language use as provided by corpora. 123 forms have been distributed in 6 groups of international students, 4 of them studying Swedish at Level I and 2 studying at Level II. </p><p>The test items in the form vary in the degree of formal, semantic and functional similarity from very close cognates, to similar words belonging to different word classes, to items exhibiting category membership and/or being in subordinate/superordinate relation to each other, to deceptive cognates. The author proposes expected similarity ratings and compares them to the results obtained. The objective measure of formal similarity is provided by a string matching algorithm, Levenshtein distance.</p><p>The similarity judgements point at the fact that intermediate similarity values can be considered problematic. Similarity ratings between somewhat similar items are usually lower than could be expected. Besides, difference in grammatical meaning lowers similarity values significantly even if lexical meaning nearly coincides. Thus, the obtained results indicate that in order to utilize similarities to facilitate language learning, more attention should be paid to underlying similarities.</p>