Coreference resolution improves extraction of Biological Expression Language statements from texts

Joint learning-based causal relation extraction from biomedical literature

Causal relation extraction of biomedical entities is one of the most complex tasks in biomedical text mining, which involves two kinds of information: entity relations and entity functions. One feasible approach is to take relation extraction and function detection as two independent sub-tasks. However, this separate learning method ignores the intrinsic correlation between them and leads to unsatisfactory performance. In this paper, we propose a joint learning model, which combines entity relation extraction and entity function detection to exploit their commonality and capture their inter-relationship, so as to improve the performance of biomedical causal relation extraction. Experimental results on the BioCreative-V Track 4 corpus show that our joint learning model outperforms the separate models in BEL statement extraction, achieving the F1 scores of 57.0% and 37.3% on the test set in Stage 2 and Stage 1 evaluations, respectively. This demonstrates that our joint learning system reaches the state-of-the-art performance in Stage 2 compared with other systems.

Natural compounds as safe therapeutic options for ulcerative colitis

Ulcerative colitis (UC) is a chronic inflammatory bowel disease of unknown etiology. Several conventional treatments for UC such as corticosteroids, immunosuppressive agents, tumor necrosis factor antagonist, integrin blockers, and interleukin antagonist, and salicylates are available but are associated with the various limitations and side-effects. None of the above treatments helps to achieve the ultimate goal of the therapy, i.e., maintenance of remission in the long-term. Natural remedies for the treatment of UC show comparatively less side effects as compared to conventional approaches, and affordable. The current review presents details on the role of herbal drugs in the treatment and cure of UC. Google, PubMed, Web of Science, and Scopus portals have been searched for potentially relevant literature to get the latest developments and updated information related to use of natural drugs in the treatment of UC. Natural products have been used over centuries to treat UC. Some of the essential herbal constituents exhibiting antiulcerogenic activity include gymnemic acid (Gymnema sylvestre), shagoal (Zingiber officinale), catechin (Camellia sinensis), curcumin (Curcuma longa), arctigenin (Arctium lappa), and boswellic acid (Boswellia serrata). Although many plant-derived products have been recommended for UC, further research to understand the exact molecular mechanism is still warranted to establish their usefulness clinically.

BioCreative VI Precision Medicine Track system performance is constrained by entity recognition and variations in corpus characteristics

Precision medicine aims to provide personalized treatments based on individual patient profiles. One critical step towards precision medicine is leveraging knowledge derived from biomedical publications-a tremendous literature resource presenting the latest scientific discoveries on genes, mutations and diseases. Biomedical natural language processing (BioNLP) plays a vital role in supporting automation of this process. BioCreative VI Track 4 brings community effort to the task of automatically identifying and extracting protein-protein interactions (PPi) affected by mutations (PPIm), important in the precision medicine context for capturing individual genotype variation related to disease.We present the READ-BioMed team's approach to identifying PPIm-related publications and to extracting specific PPIm information from those publications in the context of the BioCreative VI PPIm track. We observe that current BioNLP tools are insufficient to recognise entities for these two tasks; the best existing mutation recognition tool achieves only 55% recall in the document triage training set, while relation extraction performance is limited by the low recall performance of gene entity recognition. We develop the models accordingly: for document triage, we develop term lists capturing interactions and mutations to complement BioNLP tools, and select effective features via a feature contribution study, whereas an ensemble of BioNLP tools is employed for relation extraction.Our best document triage model achieves an F-score of 66.77% while our best model for relation extraction achieved an F-score of 35.09% over the final (updated post-task) test set. Impacting the document triage task, the characteristics of mutations are statistically different in the training and testing sets. While a vital new direction for biomedical text mining research, this early attempt to tackle the problem of identifying genetic variation of substantial biological significance highlights the importance of representative training data and the cascading impact of tool limitations in a modular system.