Mining Protein–Protein Interactions from Published Literature Using Linguamatics I2E
Natural language processing (NLP) technology can be used to rapidly extract protein–protein interactions from large collections of published literature. In this chapter we will work through a case study using MEDLINE® biomedical abstracts (1) to find how a specific set of 50 genes interact with each other. We will show what steps are required to achieve this using the I2E software from Linguamatics (www.linguamatics.com (2)).
To extract protein networks from the literature, there are two typical strategies. The first is to find pairs of proteins which are mentioned together in the same context, for example, the same sentence, with the assumption that textual proximity implies biological association. The second approach is to use precise linguistic patterns based on NLP to find specific relationships between proteins. This can reveal the direction of the relationship and its nature such as “phosphorylation” or “upregulation”. The I2E system uses a flexible text-mining approach, supporting both of these strategies, as well as hybrid strategies which fall between the two. In this chapter we show how multiple strategies can be combined to obtain high-quality results.
Key wordsProtein–protein interactions text mining natural language processing NLP knowledge discovery information extraction linguistics literature MEDLINE Linguamatics I2E
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