Triage of documents containing protein interactions affected by mutations using an NLP based machine learning approach

Abstract

Background Information on protein-protein interactions affected by mutations is very useful for understanding the biological effect of mutations and for developing treatments targeting the interactions. In this study, we developed a natural language processing (NLP) based machine learning approach for extracting such information from literature. Our aim is to identify journal abstracts or paragraphs in full-text articles that contain at least one occurrence of a protein-protein interaction (PPI) affected by a mutation. Results Our system makes use of latest NLP methods with a large number of engineered features including some based on pre-trained word embedding. Our final model achieved satisfactory performance in the Document Triage Task of the BioCreative VI Precision Medicine Track with highest recall and comparable F1-score. Conclusions The performance of our method indicates that it is ideally suited for being combined with manual annotations. Our machine learning framework and engineered features will also be very helpful for other researchers to further improve this and other related biological text mining tasks using either traditional machine learning or deep learning based methods.

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“…Early automated methods mainly fell into two categories: rule-based and machine learning-based. The rulebased approach systematically extracted specific data based on predefined rules (8)(9)(10)(11)(12)(13), while the machine learning-based approaches inferred rules from annotated data usually with much better recall and overall performance (14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29). The advent of machine learning led to more sophisticated methods that leveraged semantic information and sentence structure, resulting in significant improvements in information extraction effectiveness (19,22).…”