Revista Fineduca – 10 anos de contribuição àpesquisa em financiamento da educação

Editoria

Gene networks inference by clustering, exhaustive search and intrinsically multivariate prediction analysis.

A inferência de redes gênicas (GN) a partir de dados de expressão gênica temporal é um problema crucial e desafiador em Biologia Sistêmica. Os conjuntos de dados de expressão geralmente consistem em dezenas de amostras temporais e as redes consistem em milhares de genes, tornando inúmeros métodos de inferência inviáveis na prática. Para melhorar a escalabilidade dos métodos de inferência de GNs, esta tese propõe um arcabouço chamado GeNICE, baseado no modelo de redes gênicas probabilísticas. A principal novidade é a introdução de um procedimento de agrupamento de genes, com perfis de expressão relacionados, para fornecer uma solução aproximada com complexidade computacional reduzida. Os agrupamentos definidos são usados para reduzir a dimensionalidade permitindo uma busca exaustiva mais eficiente pelos melhores subconjuntos de genes preditores para cada gene alvo de acordo com funções critério multivariadas. GeNICE reduz consideravelmente o espaço de busca porque os candidatos a preditores ficam restritos a um gene representante por agrupamento. No final, uma análise multivariada é realizada para cada subconjunto preditor definido, visando recuperar subconjuntos mínimos para simplificar a rede gênica inferida. Em experimentos com conjuntos de dados sintéticos, GeNICE obteve uma redução substancial de tempo quando comparado a uma solução anterior sem a etapa de agrupamento, preservando a precisão da predição de expressão gênica mesmo quando o número de agrupamentos é pequeno (cerca de cinquenta) e o número de genes é grande (ordem de milhares). Para um conjunto de dados reais de microarrays de Plasmodium falciparum, a precisão da predição alcançada pelo GeNICE foi de aproximadamente 97% em média. As redes inferidas para os genes alvos da glicólise e do apicoplasto refletem propriedades topológicas de redes complexas do tipo \"mundo pequeno\" e \"livre de escala\", para os quais grande parte das conexões são estabelecidas entre os genes de um mesmo módulo e algumas poucas conexões fazem o papel de estabelecer uma ponte entre os módulos (redes mundo pequeno), e o grau de distribuição das conexões entre os genes segue uma lei de potência, na qual a maioria dos genes têm poucas conexões e poucos genes (hubs) apresentam um elevado número de conexões (redes livres de escala), como esperado.Gene network (GN) inference from temporal gene expression data is a crucial and challenging problem in Systems Biology. Expression datasets usually consist of dozens of temporal samples, while networks consist of thousands of genes, thus rendering many inference methods unfeasible in practice. To improve the scalability of GN inference methods, this work proposes a framework called GeNICE, based on Probabilistic Gene Networks; the main novelty is the introduction of a clustering procedure to group genes with related expression profiles, to provide an approximate solution with reduced computational complexity. The defined clusters were used to perform an exhaustive search to retrieve the best predictor gene subsets for each target gene, according to multivariate criterion functions. GeNICE greatly reduces the search space because predictor candidates are restricted to one representative gene per cluster. Finally, a multivariate analysis is performed for each defined predictor subset to retrieve minimal subsets and to simplify the network. In experiments with in silico generated datasets, GeNICE achieved substantial computational time reduction when compared to an existing solution without the clustering step, while preserving the gene expression prediction accuracy even when the number of clusters is small (about fifty) relative to the number of genes (order of thousands). For a Plasmodium falciparum microarray dataset, the prediction accuracy achieved by GeNICE was roughly 97% on average. The inferred networks for the apicoplast and glycolytic target genes reflects the topological properties of \"small-world\"and \"scale-free\"complex network models in which a large part of the connections is established between genes of the same functional module (smallworld networks) and the degree distribution of the connections between genes tends to form a power law, in which most genes present few connections and few genes (hubs) present a large number of connections (scale-free networks), as expected

REVISTA FINEDUCA – 10 ANOS DE CONTRIBUIÇÃO À PESQUISA EM FINANCIAMENTO DA EDUCAÇÃO

Editoria

REVISTA FINEDUCA – 10 ANOS DE CONTRIBUIÇÃO À PESQUISA EM FINANCIAMENTO DA EDUCAÇÃO

Editoria

Pairwise registration in indoor environments using adaptive combination of 2D and 3D cues

Pairwise frame registration of indoor scenes with sparse 2D local features is not particularly robust under varying lighting conditions or low visual texture. In this case, the use of 3D local features can be a solution, as such attributes come from the 3D points themselves and are resistant to visual texture and illumination variations. However, they also hamper the registration task in cases where the scene has little geometric structure. Frameworks that use both types of features have been proposed, but they do not take into account the type of scene to better explore the use of 2D or 3D features. Because varying conditions are inevitable in real indoor scenes, we propose a new framework to improve pairwise registration of consecutive frames using an adaptive combination of sparse 2D and 3D features. In our proposal, the proportion of 2D and 3D features used in the registration is automatically defined according to the levels of geometric structure and visual texture contained in each scene. The effectiveness of our proposed framework is demonstrated by experimental results from challenging scenarios with datasets including unrestricted RGB-D camera motion in indoor environments and natural changes in illuminatio

Automatic classification of prostate stromal tissue in histological images using Haralick descriptors and Local Binary Patterns

In this paper we presente a classification system that uses a combination of texture features from stromal regions: Haralick features and Local Binary Patterns (LBP) in wavelet domain. The system has five steps for classification of the tissues. First, the stromal regions were detected and extracted using segmentation techniques based on thresholding and RGB colour space. Second, the Wavelet decomposition was applied in the extracted regions to obtain the Wavelet coefficients. Third, the Haralick and LBP features were extracted from the coefficients. Fourth, relevant features were selected using the ANOVA statistical method. The classication (fifth step) was performed with Radial Basis Function (RBF) networks. The system was tested in 105 prostate images, which were divided into three groups of 35 images: normal, hyperplastic and cancerous. The system performance was evaluated using the area under the ROC curve and resulted in 0.98 for normal versus cancer, 0.95 for hyperplasia versus cancer and 0.96 for normal versus hyperplasia. Our results suggest that texture features can be used as discriminators for stromal tissues prostate images. Furthermore, the system was effective to classify prostate images, specially the hyperplastic class which is the most difficult type in diagnosis and prognosis