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

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.

Biomedical document triage using a hierarchical attention-based capsule network

Background Biomedical document triage is the foundation of biomedical information extraction, which is important to precision medicine. Recently, some neural networks-based methods have been proposed to classify biomedical documents automatically. In the biomedical domain, documents are often very long and often contain very complicated sentences. However, the current methods still find it difficult to capture important features across sentences. Results In this paper, we propose a hierarchical attention-based capsule model for biomedical document triage. The proposed model effectively employs hierarchical attention mechanism and capsule networks to capture valuable features across sentences and construct a final latent feature representation for a document. We evaluated our model on three public corpora. Conclusions Experimental results showed that both hierarchical attention mechanism and capsule networks are helpful in biomedical document triage task. Our method proved itself highly competitive or superior compared with other state-of-the-art methods.

Google Scholar and meta descriptions : does adding abstracts to a search engine results page aid in undergraduate document triage efficiency?

By focusing on the point where the document triage process interacts with a search engine results page (SERP), this experiment extends our knowledge about both SERP design and document triage behavior. Prior SERP work has shown that longer meta descriptions in SERPs improve people's ability to answer information based questions, while document triage research has shown the importance of abstracts in making relevancy decisions. Using eye tracking equipment this work employed a repeated measure within factors experimental design method replacing the existing Google Scholar (GS) SERP meta descriptions with the abstracts of the corresponding retrieved articles. Undergraduate freshmen participants were asked to use two different GS SERPs, one with a control design and one with the experimental design and determine which resources are relevant to their assigned research task. The findings show that the participants changed how long they looked at the expanded meta description, while noticeably reducing how long they gazed at other parts of the page supporting other research findings. The addition of abstracts changed user behavior by reducing how often they made surrogate level document transitions, but did not change how often they sought out full-text documents, supporting the principle of least effort. The addition of abstracts did not contribute to changes in total time on task or participant's relevancy accuracy. This study's findings conflict with other work that found that longer meta descriptions corresponded with a reduction in total task time and an improvement in accuracy for informational tasks. Further research is needed to determine if this conflict was due to task differences or if the document triage task was not challenging enough.