Mining breast cancer genetic data for improved diagnosis. (c2012)

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dc.contributor.author Zantout, Rouba Samir
dc.date.accessioned 2012-04-23T11:01:13Z
dc.date.available 2012-04-23T11:01:13Z
dc.date.copyright 2012 en_US
dc.date.issued 2012-04-23
dc.date.submitted 2012-02-10
dc.identifier.uri http://hdl.handle.net/10725/1126
dc.description Includes bibliographical references (leaves 87-89). en_US
dc.description.abstract Breast cancer is an ominous disease that affects many women; it is ranked as the fifth cause of death and the second common cancer worldwide. Analyzing breast cancer gene expression profiles for understanding genetic similarities is a very challenging problem, since a lot about the functions of many genes is still to be revealed. Computational techniques have proved reliable to support the clinics in diagnosis and therapy. In this thesis, we use a data mining method to find a logical correlation behind the clustering pattern of the genes involved in breast cancer. We design a growing hierarchical self-organizing map (GHSOM) to mine gene microarray data. GHSOM configures its topology during unsupervised learning process according to the features of the input genes microarray data, without other prior knowledge. GHSOM clusters genes that are related to each other by utilizing their microarray expression levels. We have applied GHSOM to 24,481 genes of DNA microarray of breast tumor samples from 117 patients. Our results have revealed 17 genes that are likely to be correlated, in small subsets, with four breast cancer marker genes. This result is promising for diagnosis and for better understanding of breast cancer. en_US
dc.language.iso en en_US
dc.subject Breast -- Cancer -- Diagnosis en_US
dc.subject Breast -- Cancer -- Genetic aspects en_US
dc.subject Bioinformatics en_US
dc.subject Gene expression en_US
dc.subject DNA microarrays en_US
dc.title Mining breast cancer genetic data for improved diagnosis. (c2012) en_US
dc.type Thesis en_US
dc.term.submitted Fall en_US
dc.author.degree MS in Computer Science en_US
dc.author.school Arts and Sciences en_US
dc.author.idnumber 200600448 en_US
dc.author.commembers Dr. Mirvat El-Sibai
dc.author.commembers Dr. Faisal Abu Khzam
dc.author.woa OA en_US
dc.description.physdesc 1 bound copy: xv, 92 leaves; ill. (some col.); 30 cm. available at RNL. en_US
dc.author.division Computer Science en_US
dc.author.advisor Dr. Nashaat Mansour
dc.keywords Bioinformatics en_US
dc.keywords Growing hierarchical self-organizing map en_US
dc.keywords Data mining en_US
dc.keywords Breast cancer en_US
dc.keywords Gene expression data analysis en_US
dc.keywords Microarray en_US
dc.keywords Clustering en_US
dc.keywords Selforganizing map en_US
dc.identifier.doi https://doi.org/10.26756/th.2012.5 en_US
dc.publisher.institution Lebanese American University en_US

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