Learning and Associating Phenotypic Behavior of Organisms using Biological data, M.Sc. Thesis Sharif University of Technology ; Beigy, Hamid (Supervisor) ; Motahari, Abolfazl (Supervisor)
Abstract
Datasets extracted from gene expression microarrays contain information about the phenotypic behavior of organisms. Turning this information into knowledge, i.e. finding associative genes with a given phenotype, is a daunting task. This is due to the high dimensionality of the data as the number of features on a gene expression microarray is usually very large. Moreover, a phenotype may change the expression pattern of a set of genes rather than changing each gene’s expression independently. To tackle the second problem, integrating other sources of information such as Protein-Protein Interaction (PPI) networks is required. In this thesis, the PPI network extracted from the String database...
Cataloging briefLearning and Associating Phenotypic Behavior of Organisms using Biological data, M.Sc. Thesis Sharif University of Technology ; Beigy, Hamid (Supervisor) ; Motahari, Abolfazl (Supervisor)
Abstract
Datasets extracted from gene expression microarrays contain information about the phenotypic behavior of organisms. Turning this information into knowledge, i.e. finding associative genes with a given phenotype, is a daunting task. This is due to the high dimensionality of the data as the number of features on a gene expression microarray is usually very large. Moreover, a phenotype may change the expression pattern of a set of genes rather than changing each gene’s expression independently. To tackle the second problem, integrating other sources of information such as Protein-Protein Interaction (PPI) networks is required. In this thesis, the PPI network extracted from the String database...
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