An Investigation into the Feasibility of Detecting Microscopic Disease Using Machine Learning

— The prognosis for many cancers could be improved dramatically if they could be detected while still at the microscopic disease stage. We are investigating the possibility of detecting microscopic disease using machine learning approaches based on features derived from gene expression levels and metabolic profiles. We use immunochemistry and QRT-PCR to measure the gene expression profiles from a number of antigens such as cyclin E, P27KIP1 , FHIT, Ki-67, PCNA, Bax, Bcl-2, P53, Fas, FasL and hTERT in several particular types of neuroendocrine tumors such as pheochromocytomas, paragangliomas; and the adrenocortical carcinomas (ACC), adenomas (ACA), and hyperplasia (ACH) in Cushing’s syndrome. We provide statistical evidence that, higher expression levels of hTERT, PCNA and Ki67 etc. are associated with a higher risk that the tumors are malignant or borderline, as opposed to benign. We also investigated whether higher expression levels of the P27KIP1 and FHIT etc. are associated with...

• Type: Conference
• Year: 2007
• Where: BIBE
• Authors: Mary Qu Yang, Jack Y. Yang
• Keywords: Antigens, BIBE 2007, Bioinformatics, Expression Levels, Gene Expression