Payne Philip R O, Borlawsky Tara B, Kwok Alan, Dhaval Rakesh, Greaves Andrew W
Department of Biomedical Informatics, The Ohio State University, Columbus, OH.
Summit Transl Bioinform. 2008 Mar 1;2008:85-9.
Chronic Lymphocytic Leukemia (CLL) is the most common adult leukemia in the U.S., and is currently incurable. Though a small number of biomarkers that may correlate to risk of disease progression or treatment outcome in CLL have been discovered, few have been validated in prospective studies or adopted in clinical practice. In order to address this gap in knowledge, it is desirable to discover and test hypotheses that are concerned with translational biomarker-to-phenotype correlations. We report upon a study in which commonly available ontologies were utilized to support the discovery of such translational correlations. We have specifically applied a technique known as constructive induction to reason over the contents of a research data repository utilized by the NCI-funded CLL Research Consortium. Our findings indicate that such an approach can produce semantically meaningful results that can inform hypotheses about higher-level relationships between the types of data contained in such a repository.
慢性淋巴细胞白血病（CLL）是美国最常见的成人白血病，目前无法治愈。虽然已发现少量可能与CLL疾病进展风险或治疗结果相关的生物标志物，但在前瞻性研究中得到验证或应用于临床实践的却很少。为了填补这一知识空白，需要发现并检验与转化生物标志物与表型相关性有关的假设。我们报告了一项利用常用本体来支持此类转化相关性发现的研究。我们特别应用了一种称为建设性归纳的技术，对由美国国立癌症研究所资助的CLL研究联盟使用的研究数据存储库的内容进行推理。我们的研究结果表明，这种方法可以产生语义上有意义的结果，为有关此类存储库中包含的数据类型之间更高层次关系的假设提供信息。
