Navegação Computação por Autor "Recamonde-Mendoza, Mariana"
Resultados 1-7 de 7
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An incremental gaussian mixture network for data stream classification in non-stationary environments
Diaz, Jorge Cristhian Chamby (2018) [Dissertação]Data stream classification poses many challenges for the data mining community when the environment is non-stationary. The greatest challenge in learning classifiers from data stream relates to adaptation to the concept ... -
Automatic generation of patient-specific 3D models of organs using an unified deep learning approach
Torres, Laura Amaya (2020) [Dissertação]Reconstruction of 3D shapes from images using convolutional neural networks (CNN) has become a very studied field in recent years and has demonstrated great performance. Rigid and non-rigid objects have been reconstructed ... -
Detecção e fusão de atributos duplicados para mineração de dados
Barcelos, Hortênsia Costa (2020) [Dissertação]Atributos duplicados são um problema recorrente em várias bases de dados geradas de fontes de dados similares e decentralizadas. Esta duplicação de atributos resulta em grande dimensionalidade sem aumentar proporcionalmente ... -
Explorando redes neurais de grafos para predição de interações miRNA–alvo associadas a câncer em grafos heterogêneos
Fabiano, Emanoel Aurelio Vianna (2023) [Dissertação]MicroRNAs (miRNAs) são pequenos RNAs não codificantes que desempenham um papel fundamental na regulação da expressão gênica através da ligação com RNAs mensageiros (mRNAs) alvos. Estudos recentes mostram que os miRNAs estão ... -
Exploring ensemble learning techniques to optimize the reverse engineering of gene regulatory networks
Recamonde-Mendoza, Mariana (2014) [Tese]In this thesis we are concerned about the reverse engineering of gene regulatory networks from post-genomic data, a major challenge in Bioinformatics research. Gene regulatory networks are intricate biological circuits ... -
Investigating pooling in graph neural networks for cancer genomics classification and the generalizability of pan-cancer models to cancer-specific predictions
Fontanari, Thomas Vaitses (2023) [Dissertação]New sequencing technologies have lead to a massive generation of gene expression data, enabling the analysis and modeling of the genomic aspects of critical diseases, such as cancers. In this context, machine learning (ML) ... -
Prediction of cancer driver genes with graph neural networks : a comparative analysis and a graph convolutional network-based model
Andrades, Renan Soares de (2023) [Dissertação]Identifying cancer driver genes (CDGs) is crucial for improving the understanding of cancer biology and developing effective diagnostic and treatment strategies. However, accurately identifying CDGs from a vast array of ...