An end-to-end deep learning architecture for extracting protein–protein interactions affected by genetic mutations. (18th September 2018)

• Record Type: Journal Article
• Authors: Tran, Tung; Kavuluru, Ramakanth
• Is Part Of: Database. Volume 2018(2018)
• Publication Date: 2018-09-18
• Subjects: Biology -- Databases -- Periodicals; Bioinformatics -- Periodicals; 570.285
• DOI: 10.1093/database/bay092
• Languages: English
• ISSNs: 1758-0463

Abstract

Abstract: The BioCreative VI Track IV (mining protein interactions and mutations for precision medicine) challenge was organized in 2017 with the goal of applying biomedical text mining methods to support advancements in precision medicine approaches. As part of the challenge, a new dataset was introduced for the purpose of building a supervised relation extraction model capable of taking a test article and returning a list of interacting protein pairs identified by their Entrez Gene IDs. Specifically, such pairs represent proteins participating in a binary protein–protein interaction relation where the interaction is additionally affected by a genetic mutation—referred to as a PPIm relation. In this study, we explore an end-to-end approach for PPIm relation extraction by deploying a three-component pipeline involving deep learning-based named-entity recognition and relation classification models along with a knowledge-based approach for gene normalization. We propose several recall-focused improvements to our original challenge entry that placed second when matching on Entrez Gene ID (exact matching) and on HomoloGene ID. On exact matching, the improved system achieved new competitive test results of 37.78% micro-F1 with a precision of 38.22% and recall of 37.34% that corresponds to an improvement from the prior best system by approximately three micro-F1 points. When matching on HomoloGene IDs, we report similarly competitive test results at 46.17% micro-F1 with a precision and recall of 46.67 and 45.59%, respectively, corresponding to an improvement of more than eight micro-F1 points over the prior best result. The code for our deep learning system is made publicly available at https://github.com/bionlproc/biocppi_extraction .