Text-mining of scientific literature available today is a difficult task. Many text mining algorithms have been developed in recent years. But, there are some limitations to these techniques such as speed and many of these algorithms are not available in open source. In order to be updated, the researchers need to use automated text-mining tools. Also, with so much of literature available in the biomedical field, it is impossible to manually go through these texts. So, with the help of computer algorithms and artificial intelligence techniques, we can actually discover new things by correlating the already existing biomedical literature. To support the experts in the field of Biology and Medicine, I have developed a text-mining method which outputs sentences that can be read by experts to develop relations between biomedical concepts in a given set of abstracts. The method that I have implemented is based on the Swanson’s ABC Model for Biomedical Discovery. The program searches for rare terms in the abstracts. These rare terms indicate a relation that has not yet been established. I have used functions (programmed in R language) on Vitiligo disease dataset as a source of data. With the help of experts, new relations might be discovered and this can help in better understanding of Vitiligo disease.
