Abstract
Graphical Model and Clustering-Regression based Methods for Causal Interactions: Breast Cancer Case Study
Highlights
During the last decades, cancer has been one of the main focus and concern fields for scientists
Breast Cancer is a complex disease and early detection is essential for effective treatment
In our paper we present the gene expression correlations in graphical model using the K-means clustering and linear regression methods
Summary
Cancer has been one of the main focus and concern fields for scientists. Cancer starts in cells which are the basic structural unit in the body. Cells of the cancer patient are multiplying in a way that is difficult to control, those cells cause a tumor which can be benign or premalignant. Benign tumors are usually not harmful and do not spread to other parts of the body, which is the opposite of cancer lumps. Breast Cancer is one of the most types of cancers which infects women, that is because of changes in lifestyle, increased age, and hormonal disorders [1]. To study every gene in a cell, scientists spend a lot of time when they use manual tools to monitor gene's behavior
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