Spatial Analysis on Ovarian Cancer
Project Overview
Ovarian cancer is often asymptomatic in the early stages, and patients are often diagnosed with advanced ovarian cancer, which delays the best time for treatment. The current method for detecting ovarian cancer is to draw blood to determine the biomarker: CA-125, and to use ultrasound and gynecological examination as auxiliary diagnosis. However, the specificity and accuracy of CA-125 are both insufficient, resulting in low detection accuracy. Therefore, finding a biomarker to accurately detect tumor for ovarian cancer has become an urgent issue. In recent years, microarray data analysis and data mining methods have been widely used in cancer research with good results.
Therefore, this project aims to utilize machine learning algorithms to classify and screen target genes for different stages of ovarian cancer. It is expected that this rapid screening system can be used as the basis for future ovarian cancer screening and clinical research, and to assist doctors in early diagnosis and treatment for ovarian cancer to improve the survival rate of patients.
Source: STRING
