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Automated Hypothesis Generation Based on Mining Scientific Literature

机译:基于挖掘科学文献的假设自动生成

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摘要

Keeping up with the ever-expanding flow of data and publications is untenable and poses a fundamental bottleneck to scientific progress. Current search technologies typically find many relevant documents, but they do not extract and organize the information content of these documents or suggest new scientific hypotheses based on this organized content. We present an initial case study on KnIT, a prototype system that mines the information contained in the scientific literature, represents it explicitly in a queriable network, and then further reasons upon these data to generate novel and experimentally testable hypotheses. KnIT combines entity detection with neighbor-text feature analysis and with graph-based diffusion of information to identify potential new properties of entities that are strongly implied by existing relationships. We discuss a successful application of our approach that mines the published literature to identify new protein kinases that phosphorylate the protein tumor suppressor p53. Retrospective analysis demonstrates the accuracy of this approach and ongoing laboratory experiments suggest that kinases identified by our system may indeed phosphorylate p53. These results establish proof of principle for automated hypothesis generation and discovery based on text mining of the scientific literature.
机译:跟上不断增长的数据和出版物流量是站不住脚的,并构成了科学进步的根本瓶颈。当前的搜索技术通常会找到许多相关的文档,但是它们不会提取和组织这些文档的信息内容,也不会基于此组织的内容提出新的科学假设。我们提出了一个关于KnIT的初始案例研究,KnIT是一个挖掘科学文献中包含的信息的原型系统,在可查询的网络中明确表示该信息,然后进一步根据这些数据生成新颖且可通过实验检验的假设。 KnIT将实体检测与邻居文本特征分析以及基于图的信息传播相结合,以识别现有关系强烈暗示的实体的潜在新属性。我们讨论了我们方法的成功应用,该方法挖掘了已发表的文献,以鉴定可磷酸化蛋白肿瘤抑制因子p53的新蛋白激酶。回顾性分析证明了这种方法的准确性,正在进行的实验室实验表明,我们系统鉴定出的激酶可能确实使p53磷酸化。这些结果建立了基于科学文献文本挖掘的自动假设生成和发现的原理证明。

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