eGARD: Extracting associations between genomic anomalies and drug responses from text.
eGARD: Extracting associations between genomic anomalies and drug responses from text.
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DOI：
10.1371/journal.pone.0189663
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发表时间：
2017
期刊：
影响因子：
3.7
通讯作者：
Vijay-Shanker K
中科院分区：
文献类型：
--
作者：
Mahmood ASMA;Rao S;McGarvey P;Wu C;Madhavan S;Vijay-Shanker K
肿瘤分子分析在识别基因组异常方面发挥着不可或缺的作用，这可能有助于个性化癌症治疗、改善患者的治疗结果并最大限度地降低与不同疗法相关的风险。然而，有关此类异常的临床效用证据的关键信息大部分都隐藏在生物医学文献中。对于生物管理者、临床研究人员和肿瘤学家来说，跟上快速增长的信息量和广度越来越令人望而却步，尤其是那些描述生物标志物的治疗意义并因此与治疗选择相关的信息。为了改进和加快从文献中手动审查和提取相关信息的过程，我们开发了一种基于自然语言处理 (NLP) 的文本挖掘 (TM) 系统，称为 eGARD（提取与药物反应相关的基因组异常）。该系统依赖于句子的句法性质以及各种文本特征，从 MEDLINE 摘要中提取基因组异常与药物反应之间的关系。我们的系统在内部创建和从 PharmGKB 外部获得的带注释的评估数据集上实现了分别高达 0.95、0.86 和 0.90 的高精度、召回率和 F 测量值。此外，系统提取的信息有助于确定提取的置信度，以支持管理的优先级。这样的系统将使临床研究人员能够探索使用已发表的标记物来预先对患者进行分层，以获得“最适合”的治疗，并轻松地为新的临床试验生成假设。
Tumor molecular profiling plays an integral role in identifying genomic anomalies which may help in personalizing cancer treatments, improving patient outcomes and minimizing risks associated with different therapies. However, critical information regarding the evidence of clinical utility of such anomalies is largely buried in biomedical literature. It is becoming prohibitive for biocurators, clinical researchers and oncologists to keep up with the rapidly growing volume and breadth of information, especially those that describe therapeutic implications of biomarkers and therefore relevant for treatment selection. In an effort to improve and speed up the process of manually reviewing and extracting relevant information from literature, we have developed a natural language processing (NLP)-based text mining (TM) system called eGARD (extracting Genomic Anomalies association with Response to Drugs). This system relies on the syntactic nature of sentences coupled with various textual features to extract relations between genomic anomalies and drug response from MEDLINE abstracts. Our system achieved high precision, recall and F-measure of up to 0.95, 0.86 and 0.90, respectively, on annotated evaluation datasets created in-house and obtained externally from PharmGKB. Additionally, the system extracted information that helps determine the confidence level of extraction to support prioritization of curation. Such a system will enable clinical researchers to explore the use of published markers to stratify patients upfront for ‘best-fit’ therapies and readily generate hypotheses for new clinical trials.
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