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Articles 1 - 3 of 3
Full-Text Articles in Bioinformatics
Deep Learning Applications In Medical Bioinformatics, Ziad Omar
Deep Learning Applications In Medical Bioinformatics, Ziad Omar
Electronic Theses and Dissertations
After a patient’s breast cancer diagnosis, identifying breast cancer lymph node metastases is one of the most important and critical factor that is directly related to the patient’s survival. The traditional way to examine the existence of cancer cells in the breast lymph nodes is through a lymph node procedure, biopsy. The procedure process is time-consuming for the patient and the provider, costly, and lacks accuracy as not every lymph node is examined. The intent of this study is to develop an artificial neural network (ANNs) that would map genetic biomarkers to breast lymph node classes using ANNs. The neural …
Deciphering The Role Of Human Arylamine N-Acetyltransferase 1 (Nat1) In Breast Cancer Cell Metabolism Using A Systems Biology Approach., Samantha Marie Carlisle
Deciphering The Role Of Human Arylamine N-Acetyltransferase 1 (Nat1) In Breast Cancer Cell Metabolism Using A Systems Biology Approach., Samantha Marie Carlisle
Electronic Theses and Dissertations
Background: Human arylamine N-acetyltransferase 1 (NAT1) is a phase II xenobiotic metabolizing enzyme found in almost all tissues. NAT1 can additionally hydrolyze acetyl-coenzyme A (acetyl-CoA) in the absence of an arylamine substrate. NAT1 expression varies inter-individually and is elevated in several cancers including estrogen receptor positive (ER+) breast cancers. Additionally, multiple studies have shown the knockdown of NAT1, by both small molecule inhibition and siRNA methods, in breast cancer cells leads to decreased invasive ability and proliferation and decreased anchorage-independent colony formation. However, the exact mechanism by which NAT1 expression affects cancer risk and progression remains unclear. Additionally, consequences …
Expression Of Genes For Peptide/Protein Hormones And Their Cognate Receptors In Breast Carcinomas As Biomarkers Predicting Risk Of Recurrence., Michael Wesley Daniels
Expression Of Genes For Peptide/Protein Hormones And Their Cognate Receptors In Breast Carcinomas As Biomarkers Predicting Risk Of Recurrence., Michael Wesley Daniels
Electronic Theses and Dissertations
Certain hormones and/or receptors influencing normal cellular pathways were detected in breast cancers. The hypothesis is that gene subsets predict risk of breast carcinoma recurrence in patients with primary disease. Gene expression of 55 hormones and 73 receptors were determined by microarray with LCM-procured carcinoma cells of 247 de-identified biopsies. Univariate and multivariate Cox regressions were determined using expression levels of each hormone/receptor gene, individually or as a pair. Significant genes derived for each subset were analyzed to predict risk of cancer recurrence with 1000 LASSO training/test sets. A 14-gene molecular signature was identified for predicting clinical outcome without regard …