A novel corpus of molecular to higher-order events that facilitates the understanding of the pathogenic mechanisms of idiopathic pulmonary fibrosis

• Published in: Scientific reports Vol. 13; no. 1; p. 5986
• Main Authors: Nagano, Nozomi, Tokunaga, Narumi, Ikeda, Masami, Inoura, Hiroko, Khoa, Duong A., Miwa, Makoto, Sohrab, Mohammad G., Topić, Goran, Nogami-Itoh, Mari, Takamura, Hiroya
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 12.04.2023; Nature Publishing Group; Nature Portfolio
• Subjects: 631/114/2406; 692/308/2778; 692/4017; 692/420; 692/699/1785; COVID-19; Data Mining; Fibrosis; Humanities and Social Sciences; Humans; Idiopathic Pulmonary Fibrosis - pathology; Information processing; Lung cancer; Lung diseases; Lung Diseases, Interstitial; Lung Neoplasms; multidisciplinary; Pulmonary fibrosis; Science; Science (multidisciplinary)
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-023-32915-8

Idiopathic pulmonary fibrosis (IPF) is a severe and progressive chronic fibrosing interstitial lung disease with causes that have remained unclear to date. Development of effective treatments will require elucidation of the detailed pathogenetic mechanisms of IPF at both the molecular and cellular levels. With a biomedical corpus that includes IPF-related entities and events, text-mining systems can efficiently extract such mechanism-related information from huge amounts of literature on the disease. A novel corpus consisting of 150 abstracts with 9297 entities intended for training a text-mining system was constructed to clarify IPF-related pathogenetic mechanisms. For this corpus, entity information was annotated, as were relation and event information. To construct IPF-related networks, we also conducted entity normalization with IDs assigned to entities. Thereby, we extracted the same entities, which are expressed differently. Moreover, IPF-related events have been defined in this corpus, in contrast to existing corpora. This corpus will be useful to extract IPF-related information from scientific texts. Because many entities and events are related to lung diseases, this freely available corpus can also be used to extract information related to other lung diseases such as lung cancer and interstitial pneumonia caused by COVID-19.