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- W4385011796 abstract "Gene-disease association plays a crucial role in healthcare knowledge discovery, and a large amount of valuable information is hidden in the literature. To alleviate this problem, we designed and organized the Gene-Disease Association Semantics (GDAS) track in CHIP2022, which aims to automatically extract the semantic association between the gene and disease from the literature. The GDAS track includes three progressive subtasks, gene-disease concept recognization, semantic role labeling, and gene-regulation-disease triplet extraction. Six teams participated in the track and submitted valid results, three of which showed promising performance in the GDAS track, we briefly present and summarize their methods. Finally, we discuss the potential value of the GDAS track to the healthcare and BioNLP communities, and explore the feasibility of further methods to facilitate the GDAS track." @default.
- W4385011796 created "2023-07-22" @default.
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- W4385011796 date "2023-01-01" @default.
- W4385011796 modified "2023-10-16" @default.
- W4385011796 title "Text Mining Task for “Gene-Disease” Association Semantics in CHIP 2022" @default.
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- W4385011796 doi "https://doi.org/10.1007/978-981-99-4826-0_1" @default.
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