Biyomedikal varlıklar arasındaki ilişkilerin biyomedikal makaleler aracılığıyla keşfedilmesine dair bir sistem geliştirilmesi

In today's world, digitalization is spreading rapidly. This proliferation, while making our lives easier on the one hand, also brings new challenges, such as the analysis and processing of large amounts of digital data on the other. This is particularly evident in the context of academic research. Academic research necessitates sophisticated evaluation procedures. In this context, it is known that research conducted on diseases should be evaluated effectively. In conclusion, in this study, publications related to diseases were subjected to text analysis methods and then transformed into a network structure that allows the association of data with significant biomedical connections. It is aimed at examining the complex network structure of two biomedical entities that have important connections, such as therapeutic and causative. In this case, it has been confirmed that the entity pairs obtained through manual search methods are real connections. This study has successfully resolved the often time-consuming manual search process for locating existing known biomedical entities. Additionally, through this method, there is potential to discover unknown or yet-to-be-explored possible new relationships (therapeutic, causative, etc.) via multiple binary connection patterns. In conclusion, the combination of techniques such as graph analysis, knowledge discovery, and text mining is leading to the discovery of potentially significant new findings in biomedical research.