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Learning and Associating Phenotypic Behavior of Organisms using Biological data

Mehrabi, Aslan | 2015

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  1. Type of Document: M.Sc. Thesis
  2. Language: Farsi
  3. Document No: 48031 (19)
  4. University: Sharif University of Technology
  5. Department: Computer Engineering
  6. Advisor(s): Beigy, Hamid; Motahari, Abolfazl
  7. Abstract:
  8. 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 is used to select sets of genes such that their co-expression might be related to the given phenotype. Expressions of sets of genes taken from PPI networks are compared between cases and controls using the statistical methods in order to increase the number of associative gene candidates.
    It is shown that there exists sets of genes that are usually removed from the set of candidates due to their individual weak signals can be detected by considering them as a group. The comparison of selected genes by this method with the selection of other known methods, showed the success of the procedure of the proposed method. This comparison was done by comparing the number of similar genes selected by the methods and the reported genes of the phenotype related to the examined microarray dataset
  9. Keywords:
  10. Statistical Methods ; Microarray Data ; Protein-Protein Interaction ; Feature Selection ; Bioinformatics ; Microarray Experiment

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