Yujing Xue
ABSTRACT
The clustering of functionally related genes has been an important task for biologists. With the recent progress of machine learning technology, researchers now have more powerful weapons to identify the structures within a large amount of DNA sequencing data. That allows the research on genes to be conducted in an efficient and scalable way. This paper studies the clustering of functionally related genes and their impact on the development and prognosis of lung cancer with machine learning technologies. The patient data derived from 218 patients are analyzed. We focus on two extreme cases, one case includes patients who survived less than 1 year, and the other case includes patients who survived longer than 5 years. We will investigate how different clustering methods can assist in the visualization of the DNA sequence data of such patients, and how such methods can help us identify the underlying patterns of the DNA sequence data.
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