Text mining for literature review and knowledge discovery in cancer risk assessment and research.
Text mining for literature review and knowledge discovery in cancer risk assessment and research.
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DOI：
10.1371/journal.pone.0033427
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发表时间：
2012
期刊：
影响因子：
3.7
通讯作者：
Stenius U
中科院分区：
文献类型：
--
作者：
Korhonen A;Séaghdha DO;Silins I;Sun L;Högberg J;Stenius U
生物医学文本挖掘的研究已经开始产生一种技术，可以使生物科学家更容易地获取生物医学文献中的信息。当前的挑战之一是整合和完善这项技术，以支持生物医学中的现实科学任务，并评估其在这些任务背景下的实用性。我们描述螃蟹-一个完全集成的文本挖掘工具，旨在支持化学品健康风险评估。这项任务既复杂又耗时，需要对某一特定化学品的现有科学数据进行彻底审查。涵盖了来自生物医学各个领域的人类、动物、细胞和其他机械数据，这是高度多样化的，因此很难通过手工手段从文献数据库中获取。我们的工具通过从已发表的文献中提取相关的科学数据，并根据多个定性维度对其进行分类，从而实现了这一过程的自动化。该工具是与风险评估人员密切合作开发的，允许以各种方式导航分类数据集，并与其他用户共享数据。我们提出了一个直接的和基于用户的评估，表明该工具中集成的技术是高度准确的，并报告了一些案例研究，这些案例研究表明该工具如何用于支持癌症风险评估和研究中的科学发现。我们的工作证明了文本挖掘管道在促进生物医学复杂研究任务中的有用性。我们还讨论了未来我们的技术在其他类型化学品风险评估中的进一步发展和应用。
Research in biomedical text mining is starting to produce technology which can make information in biomedical literature more accessible for bio-scientists. One of the current challenges is to integrate and refine this technology to support real-life scientific tasks in biomedicine, and to evaluate its usefulness in the context of such tasks. We describe CRAB – a fully integrated text mining tool designed to support chemical health risk assessment. This task is complex and time-consuming, requiring a thorough review of existing scientific data on a particular chemical. Covering human, animal, cellular and other mechanistic data from various fields of biomedicine, this is highly varied and therefore difficult to harvest from literature databases via manual means. Our tool automates the process by extracting relevant scientific data in published literature and classifying it according to multiple qualitative dimensions. Developed in close collaboration with risk assessors, the tool allows navigating the classified dataset in various ways and sharing the data with other users. We present a direct and user-based evaluation which shows that the technology integrated in the tool is highly accurate, and report a number of case studies which demonstrate how the tool can be used to support scientific discovery in cancer risk assessment and research. Our work demonstrates the usefulness of a text mining pipeline in facilitating complex research tasks in biomedicine. We discuss further development and application of our technology to other types of chemical risk assessment in the future.
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影响因子：
7.2
作者：
FEINSTEIN, AR;CICCHETTI, DV
通讯作者：
CICCHETTI, DV
影响因子：
4.3
作者：
Frijters, Raoul;van Vugt, Marianne;Alkema, Wynand
通讯作者：
Alkema, Wynand
影响因子：
7.2
作者：
BYRT, T;BISHOP, J;CARLIN, JB
通讯作者：
CARLIN, JB
影响因子：
10.4
作者：
Judson R;Richard A;Dix DJ;Houck K;Martin M;Kavlock R;Dellarco V;Henry T;Holderman T;Sayre P;Tan S;Carpenter T;Smith E
通讯作者：
Smith E
DOI：
10.1002/asi.21414
发表时间：
2010-12-01
影响因子：
--
作者：
Keselman, Alla;Rosemblat, Graciela;Kilicoglu, Halil;Fiszman, Marcelo;Jin, Honglan;Shin, Dongwook;Rindflesch, Thomas C.
通讯作者：
Rindflesch, Thomas C.