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Text mining and pattern clustering for relation extraction of breast cancer and related genes

With the number increase of biomedical literatures, biomedical relation extraction discovery from the literature represents a new challenge for researchers in recent years. Then, a system that automatically extracts the related genes to the targeted disease is required. In this paper, we explore text mining and pattern clustering for relation extraction of breast cancer and related genes. It can be considered an unsupervised method and labeled data is not necessary. We firstly extract the candidate genes related to breast cancer by checking the window distance between the appearance of genes and breast cancer in a sentence. Then, two different clustering approaches (simple clustering and K-means clustering) are applied for finding the candidate association words that indicate the relationship between breast cancer and genes. The comparison experiment demonstrates that simple clustering is superior to K-means clustering in this task.