Savova Guergana K, Tseytlin Eugene, Finan Sean, Castine Melissa, Miller Timothy, Medvedeva Olga, Harris David, Hochheiser Harry, Lin Chen, Chavan Girish, Jacobson Rebecca S
Boston Children's Hospital, Boston, Massachusetts.
Harvard Medical School, Boston, Massachusetts.
Cancer Res. 2017 Nov 1;77(21):e115-e118. doi: 10.1158/0008-5472.CAN-17-0615.
Precise phenotype information is needed to understand the effects of genetic and epigenetic changes on tumor behavior and responsiveness. Extraction and representation of cancer phenotypes is currently mostly performed manually, making it difficult to correlate phenotypic data to genomic data. In addition, genomic data are being produced at an increasingly faster pace, exacerbating the problem. The DeepPhe software enables automated extraction of detailed phenotype information from electronic medical records of cancer patients. The system implements advanced Natural Language Processing and knowledge engineering methods within a flexible modular architecture, and was evaluated using a manually annotated dataset of the University of Pittsburgh Medical Center breast cancer patients. The resulting platform provides critical and missing computational methods for computational phenotyping. Working in tandem with advanced analysis of high-throughput sequencing, these approaches will further accelerate the transition to precision cancer treatment. .
需要精确的表型信息来了解基因和表观遗传变化对肿瘤行为及反应性的影响。目前,癌症表型的提取和呈现大多是手动进行的,这使得将表型数据与基因组数据相关联变得困难。此外,基因组数据的产生速度越来越快,使问题更加严重。DeepPhe软件能够从癌症患者的电子病历中自动提取详细的表型信息。该系统在灵活的模块化架构中实现了先进的自然语言处理和知识工程方法,并使用匹兹堡大学医学中心乳腺癌患者的手动注释数据集进行了评估。由此产生的平台为计算表型分析提供了关键且缺失的计算方法。与高通量测序的先进分析协同工作,这些方法将进一步加速向精准癌症治疗的转变。