Agrupamento de sequências de miRNA utilizando aprendizado não-supervisionado baseado em grafos

• Ano de defesa: 2016
• Autor(a) principal: Kasahara, Viviani Akemi
• Orientador(a): Nicoletti, Maria do Carmo
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Federal de São Carlos; Câmpus São Carlos
• Programa de Pós-Graduação: Programa de Pós-Graduação em Ciência da Computação - PPGCC
• Palavras-chave em Português: Agrupamento de miRNA; Mineração de dados; Teoria dos grafos; Algoritmo espectral; Aprendizado não-supervisionado
• Palavras-chave em Inglês: Clustering; miRNA clustering; Data mining; Unsupervised learning; Graph theory; Graph based clustering algorithms; Algorithm based on region of influence; Algorithm based on minimum spanning tree; Spectral algorithm
• Área do conhecimento CNPq: CIENCIAS EXATAS E DA TERRA::CIENCIA DA COMPUTACAO
• Link de acesso: https://repositorio.ufscar.br/handle/20.500.14289/8124

Cluster analysis is the organization of a collection of patterns into clusters based on similarity which is determined by using properties of data. Clustering techniques can be useful in a variety of knowledge domains such as biotechnology, computer vision, document retrieval and many others. An interesting area of biology involves the concept of microRNAs (miRNAs) that are approximately 22 nucleotide-long non-coding RNA molecules that play important roles in gene regulation. Clustering miRNA sequences can help to understand and explore sequences belonging to the same cluster that has similar biological functions. This research work investigates and explores seven unsupervised clustering algorithms based on graphs that can be divided into three categories: algorithm based on region of influence, algorithm based on minimum spanning tree and spectral algorithm. To assess the contribution of the proposed algorithms, data from miRNA families stored in the online miRBase database were used in the conducted experiments. The results of these experiments were presented, analysed and evaluated using clustering validation indexes as well as visual analysis.