  With the proliferation of research means and computational methodologies,
published biomedical literature is growing exponentially in numbers and volume.
Cancer cell lines are frequently used models in biological and medical research
that are currently applied for a wide range of purposes, from studies of
cellular mechanisms to drug development, which has led to a wealth of related
data and publications. Sifting through large quantities of text to gather
relevant information on the cell lines of interest is tedious and extremely
slow when 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 https://cancercelllines.org
