Text mining for contexts and relationships in cancer genomics literature.

Text mining for contexts and relationships in cancer genomics literature.
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DOI:
10.1093/bioinformatics/btae021
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发表时间:
2024-01-02
期刊:
Bioinformatics (Oxford, England)
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其他
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科学进步建立在现有研究成果的基础上。2001年人类基因组的发表导致了大量文献的产生,这些文献探索了基因的特定环境功能和相互作用。需要技术来对研究论文进行大规模的文本挖掘,以提取特定实验背景和细胞状态(如癌症)中基因的报道作用,从而促进新的治疗策略的设计。我们提出了一种新的语料库和文本挖掘方法,可以准确地从生物医学文本中识别和提取癌症基因组学实验的最重要细节。我们建立了一个命名实体识别模型,该模型可以准确地从PubMed摘要文本中提取相关实验细节,第二个模型可以识别它们之间的关系。该系统优于早期的模型,并能够在不同的和动态发展的实验环境中分析基因功能。代码和数据可在这里获得:https://github.com/cambridgeltl/functional-genomics-ie。
Scientific advances build on the findings of existing research. The 2001 publication of the human genome has led to the production of huge volumes of literature exploring the context-specific functions and interactions of genes. Technology is needed to perform large-scale text mining of research papers to extract the reported actions of genes in specific experimental contexts and cell states, such as cancer, thereby facilitating the design of new therapeutic strategies. We present a new corpus and Text Mining methodology that can accurately identify and extract the most important details of cancer genomics experiments from biomedical texts. We build a Named Entity Recognition model that accurately extracts relevant experiment details from PubMed abstract text, and a second model that identifies the relationships between them. This system outperforms earlier models and enables the analysis of gene function in diverse and dynamically evolving experimental contexts. Code and data are available here: https://github.com/cambridgeltl/functional-genomics-ie.
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