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The community of different types of microbes present in a biological nicheplays a very important role in functioning of the system. The crosstalk orinteractions among the different microbes contributes to the building blocks ofsuch microbial community structures. Evidence reported in biomedical textserves as a reliable source for predicting such interactions. However, goingthrough the vast and ever-increasing volume of biomedical literature is anintimidating and time consuming process. This necessitates development ofautomated methods capable of accurately extracting bacterial relations reportedin biomedical literature. In this paper, we introduce a method for automatedextraction of microbial interactions (specifically between bacteria) frombiomedical literature along with ways of using transfer learning to improve itsaccuracy. We also describe a pipeline using which relations among specificbacteria groups can be mined. Additionally, we introduce the first publiclyavailable dataset which can be used to develop bacterial interaction extractionmethods.

BactInt: A domain driven transfer learning approach and a corpus for extracting inter-bacterial interactions from biomedical text