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Preeclampsia is a leading cause of maternal and fetal morbidity and mortality. Currently, the only definitive treatment of preeclampsia is delivery of the placenta, which is central to the pathogenesis of the disease. Transcriptional profiling of human placenta from pregnancies complicated by preeclampsia has been extensively performed to identify differentially expressed genes (DEGs). The decisions to investigate DEGs experimentally are biased by many factors, causing many DEGs to remain uninvestigated. A set of DEGs which are associated with a disease experimentally, but which have no known association to the disease in the literature are known as the ignorome. Preeclampsia has an extensive body of scientific literature, a large pool of DEG data, and only one definitive treatment. Tools facilitating knowledge-based analyses, which are capable of combining disparate data from many sources in order to suggest underlying mechanisms of action, may be a valuable resource to support discovery and improve our understanding of this disease. In this work we demonstrate how a biomedical knowledge graph (KG) can be used to identify novel preeclampsia molecular mechanisms. Existing open source biomedical resources and publicly available high-throughput transcriptional profiling data were used to identify and annotate the function of currently uninvestigated preeclampsia-associated DEGs. Experimentally investigated genes associated with preeclampsia were identified from PubMed abstracts using text-mining methodologies. The relative complement of the text-mined- and meta-analysis-derived lists were identified as the uninvestigated preeclampsia-associated DEGs (n=445), i.e., the preeclampsia ignorome. Using the KG to investigate relevant DEGs revealed 53 novel clinically relevant and biologically actionable mechanistic associations., Comment: Preprint of an article submitted for consideration in Pacific Symposium on Biocomputing \copyright 2022 copyright World Scientific Publishing Company https://psb.stanford.edu/

Named entity recognition tools are used to identify mentions of biomedical entities in free text and are essential components of high-quality information retrieval and extraction systems. Without good entity recognition, methods will mislabel searched text and will miss important information or identify spurious text that will frustrate users. Most tools do not capture non-contiguous entities which are separate spans of text that together refer to an entity, e.g., the entity “type 1 diabetes” in the phrase “type 1 and type 2 diabetes.” This type is commonly found in biomedical texts, especially in lists, where multiple biomedical entities are named in shortened form to avoid repeating words. Most text annotation systems, that enable users to view and edit entity annotations, do not support non-contiguous entities. Therefore, experts cannot even visualize non-contiguous entities, let alone annotate them to build valuable datasets for machine learning methods. To combat this problem and as part of the BLAH6 hackathon, we extended the TextAE platform to allow visualization and annotation of non-contiguous entities. This enables users to add new subspans to existing entities by selecting additional text. We integrate this new functionality with TextAE’s existing editing functionality to allow easy changes to entity annotation and editing of relation annotations involving non-contiguous entities, with importing and exporting to the PubAnnotation format. Finally, we roughly quantify the problem across the entire accessible biomedical literature to highlight that there are a substantial number of non-contiguous entities that appear in lists that would be missed by most text mining systems.

관광객과 어촌주민의 필요에 의해 대안관광의 하나로 어촌관광이 등장했다. 본 연구는 어촌관광의 중요한 구성요소임에도 불구하고, 연구가 부진한 어촌체험 프로그램을 연구의 주제로 하였다. 어촌체험 프로그램 개발방향을 모색하기 위해, 어촌어항협회에서 운영하고 있는 바다여행 포털 해양관광정보포털(www.seatour.com)에 등록된 94개 어촌체험마을의 529개 체험 프로그램을 연구대상으로 하였다. 연구 내용은 체험프로그램을 활용자원과 활동내용을 기준으로 지역별 분석을 실시하고 이곳에서 나타나는 문제점을 토대로 개발방향을 제시하였다. 활용자원을 기준으로 분석한 결과 바다 및 해양생물과 어구와 어법을 단순 활용한 프로그램이 절대적이었고, 역사문화와 주변환경을 활용한 프로그램은 저조했다. 활동내용을 기준으로 분석을 실시한 결과, 특정도구와 맨손으로 해산물을 잡는 활동이 압도적으로 높게 나타났고, 어민생활 및 역사문화체험, 교육 및 관찰은 아주 낮게 나타났다. 활용자원과 활동내용의 지역별 특성은 체험프로그램이 자연자원을 주로 활용한 결과 동해안, 서해안, 남해안 지역으로 나뉘어 졌다. 향후 우리나라 어촌체험 프로그램의 발전을 위해서는 단순한 자연자원을 이용하는 일시적, 소모적, 무계획적인 단계를 넘어, 지역 특성을 나타내는 생활문화와 역사문화, 그리고 레포츠로 자원을 다양화 시키고, 단순히 소모적인 활동이 아니라 교육, 관찰, 감상과 같은 활동으로 확대가 요구된다.