Medicine and Health Sciences Commons^™

Building A Statistical Model For Predicting Cancer Genes, Ivan P. Gorlov, Christopher J. Logothetis, Shenying Fang, Olga Y. Gorlova, Christopher Amos

Dartmouth Scholarship

More than 400 cancer genes have been identified in the human genome. The list is not yet complete. Statistical models predicting cancer genes may help with identification of novel cancer gene candidates. We used known prostate cancer (PCa) genes (identified through KnowledgeNet) as a training set to build a binary logistic regression model identifying PCa genes. Internal and external validation of the model was conducted using a validation set (also from KnowledgeNet), permutations, and external data on genes with recurrent prostate tumor mutations. We evaluated a set of 33 gene characteristics as predictors. Sixteen of the original 33 predictors were …

A Heuristic Solution Of The Identifiability Problem Of The Age-Period-Cohort Analysis Of Cancer Occurrence: Lung Cancer Example., Tengiz Mdzinarishvili, Simon Sherman

Journal Articles: Eppley Institute

BACKGROUND: The Age-Period-Cohort (APC) analysis is aimed at estimating the following effects on disease incidence: (i) the age of the subject at the time of disease diagnosis; (ii) the time period, when the disease occurred; and (iii) the date of birth of the subject. These effects can help in evaluating the biological events leading to the disease, in estimating the influence of distinct risk factors on disease occurrence, and in the development of new strategies for disease prevention and treatment.

METHODOLOGY/PRINCIPAL FINDINGS: We developed a novel approach for estimating the APC effects on disease incidence rates in the frame of …