With the proliferation of research means and computational methodologies,
published biomedical literature is growing exponentially in numbers and volume.
As a consequence, in the fields of biological, medical and clinical research,
domain experts have to sift through massive amounts of scientific text to find
relevant information. However, this process is extremely tedious and slow to be
performed by humans. Hence, novel computational information extraction and
correlation mechanisms are required to boost meaningful knowledge extraction.
In this work, we present the design, implementation and application of a novel
data extraction and exploration system. This system extracts deep semantic
relations between textual entities from scientific literature to enrich
existing structured clinical data in the domain of cancer cell lines. We
introduce a new public data exploration portal, which enables automatic linking
of genomic copy number variants plots with ranked, related entities such as
affected genes. Each relation is accompanied by literature-derived evidences,
allowing for deep, yet rapid, literature search, using existing structured data
as a springboard. Our system is publicly available on the web at
this https URL

本文介绍了一种新的数据提取和探索系统，可以从癌细胞系的科学文献中提取文本实体之间的深层次语义关系，为现有结构化临床数据提供丰富的语义信息，并自动链接基因组拷贝数变异图与相关实体，以及相关文献证据，从而实现更为快捷深入的文献检索。