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Full-Text Articles in Medicine and Health Sciences
Analyzing Prominent Genes In Acute Lymphocytic Leukemia (All), Shima Z. Omar
Analyzing Prominent Genes In Acute Lymphocytic Leukemia (All), Shima Z. Omar
Honors College Theses
Acute lymphocytic leukemia (ALL) is the most common type of childhood cancer. Leukemia is a type of cancer that involves the bone marrow and blood. This research study examined prominent genes in the disease. Two groups of genes, tumor suppressor and cell differentiation, were compared using statistical analysis to compare their binding potential and epigenetic potential. It is most likely that I failed to detect significant differences either because these genes’ function in the disease etiology is not strongly contexed to changes in expression, or that the magnitude of the differences were too slight to be detected with these methods. …
Analyzing The Phenotypic Effect Of Three Candidate Genes Associated With Nonsyndromic Craniosynostosis Using A Zebrafish Model, Megan A. Hept
Analyzing The Phenotypic Effect Of Three Candidate Genes Associated With Nonsyndromic Craniosynostosis Using A Zebrafish Model, Megan A. Hept
Theses and Dissertations
In normal cranial suture development, the cranial sutures close at predetermined periods of development to allow the brain the capability to grow in a malleable environment. However, in craniosynostosis, cranial sutures prematurely fuse before birth which can lead to a wide range of developmental issues and complications. Craniosynostosis can be categorized as nonsyndromic which involves the sole fusion of one or more of the cranial sutures, or syndromic in which cranial sutures fuse as well as other abnormalities associated with a genetic disorder. Past research has identified three candidate genes that could be possible disease causing mutations in nonsyndromic sagittal …
Data Mining The Functional Characterizations Of Proteins To Predict Their Cancer-Relatedness, Peter Revesz, Christopher Assi
Data Mining The Functional Characterizations Of Proteins To Predict Their Cancer-Relatedness, Peter Revesz, Christopher Assi
School of Computing: Faculty Publications
This paper considers two types of protein data. First, data about protein function described in a number of ways, such as, GO terms and PFAM families. Second, data about whether individual proteins are experimentally associated with cancer by an anomalous elevation or lowering of their expressions within cancerous cells. We combine these two types of protein data and test whether the first type of data, that is, the functional descriptors, can predict the second type of data, that is, cancer-relatedness. By using data mining and machine learning, we derive a classifier algorithm that using only GO term and PFAM family …