Relation Extraction between Biomedical Entities from Literature using Semi- Supervised Learning Approach

Now-a-days, people around the world are infected by many new diseases. The cost of developing or discovering a new drug for the newly discovered disease is very high and prolonged process. These could be eliminated with the help of already existing resources. To identify the candidates from the existing drugs, we need to extract the relation between the drug, target and disease by textming a large-scale literature. Recently, computational approaches which is used for identifying the relationships between the entities in biomedical domain are appearing as an active area of research for drug discovery as it needs more man power. Due to the limited computational approaches, the relation extraction between drug-gene and genedisease association from the unstructured biomedical documents is very hard. In this work, we proposed a semi-supervised approach named pattern based bootstrapping method to extract the direct relations between drug, gene and disease from the biomedical literature. These direct relationships are used to infer indirect relationships between entities such as drug and disease. Now these indirect relationships are used to determine the new candidates for drug repositioning which in turn will reduce the time and the patient’s risk.