Wager Kim, Chari Dheepa, Ho Steffan, Rees Tomas, Penner Orion, Schijvenaars Bob Ja
Oxford PharmaGenesis, Oxford, UK.
Pfizer, Inc., New York, NY, USA.
Cancer Inform. 2022 Mar 22;21:11769351221086441. doi: 10.1177/11769351221086441. eCollection 2022.
Biomarkers, as measurements of defined biological characteristics, can play a pivotal role in estimations of disease risk, early detection, differential diagnosis, assessment of disease progression and outcomes prediction. Studies of cancer biomarkers are published daily; some are well characterized, while others are of growing interest. Managing this flow of information is challenging for scientists and clinicians. We sought to develop a novel text-mining method employing biomarker co-occurrence processing applied to a deeply indexed full-text database to generate time-interval-delimited biomarker co-occurrence networks. Biomarkers across 6 cancer sites and a cancer-agnostic network were successfully characterized in terms of their emergence in the published literature and the context in which they are described. Our approach, which enables us to find publications based on biomarker relationships, identified biomarker relationships not known to existing interaction networks. This search method finds relevant literature that could be missed with keyword searches, even if full text is available. It enables users to extract relevant biological information and may provide new biological insights that could not be achieved by individual review of papers.
生物标志物作为特定生物学特征的测量指标，在疾病风险评估、早期检测、鉴别诊断、疾病进展评估及预后预测中可发挥关键作用。关于癌症生物标志物的研究每天都有发表；有些已得到充分表征，而另一些则越来越受关注。对科学家和临床医生而言，管理这些信息流颇具挑战性。我们试图开发一种新颖的文本挖掘方法，该方法采用生物标志物共现处理，应用于深度索引的全文数据库，以生成按时间间隔划分的生物标志物共现网络。已成功根据6个癌症部位的生物标志物在已发表文献中的出现情况及其描述背景对其进行了表征，并构建了一个与癌症无关的网络。我们的方法能够根据生物标志物关系查找出版物，识别出了现有相互作用网络未知的生物标志物关系。这种搜索方法能找到即便有全文也可能通过关键词搜索遗漏的相关文献。它使用户能够提取相关生物学信息，并可能提供通过单独审阅论文无法获得的新生物学见解。
