Natural Language Processing Approaches for Retrieval of Clinically Relevant Genomic Information in Cancer.
Natural Language Processing Approaches for Retrieval of Clinically Relevant Genomic Information in Cancer.
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DOI：
10.3233/shti220735
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发表时间：
2022-06-29
影响因子：
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通讯作者：
Anagnostou, Valsamo
中科院分区：
文献类型：
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作者：
Botsis, Taxiarchis;Murray, Joseph;Leal, Alessandro;Palsgrove, Doreen;Wang, Wei;White, James R;Velculescu, Victor E;Anagnostou, Valsamo
基因组数据在临床决策中的加速影响已经产生了从基于肿瘤解剖起源的治疗到结合关键基因组特征以指导治疗的范式转变。评估患者癌症基因组背景的临床有效性和实用性代表了肿瘤学实践中的新兴挑战之一，要求开发用于从医学文本中提取临床相关基因组信息的自动化平台。我们开发了PubMiner，一种自然语言处理工具，用于从生物医学摘要中提取和解释癌症类型，治疗和基因组信息。我们最初的重点是检索基因名称，变体和否定，其中PubMiner在总召回率（91.7%）方面表现出色，精确度为79.7%。我们的下一步包括开发一个基于网络的界面，以促进基于每个肿瘤独特基因组指纹的个性化治疗。
The accelerating impact of genomic data in clinical decision-making has generated a paradigm shift from treatment based on the anatomic origin of the tumor to the incorporation of key genomic features to guide therapy. Assessing the clinical validity and utility of the genomic background of a patient’s cancer represents one of the emerging challenges in oncology practice, demanding the development of automated platforms for extracting clinically relevant genomic information from medical texts. We developed PubMiner, a natural language processing tool to extract and interpret cancer type, therapy, and genomic information from biomedical abstracts. Our initial focus has been the retrieval of gene names, variants, and negations, where PubMiner performed highly in terms of total recall (91.7%) with a precision of 79.7%. Our next steps include developing a web-based interface to promote personalized treatment based on each tumor’s unique genomic fingerprints.