Data-Driven Information Extraction and Enrichment of Molecular Profiling Data for Cancer Cell Lines
This addresses the tedious and slow manual process of sifting through large volumes of text for researchers in cancer biology and drug development, though it is incremental as it builds on existing computational methods.
The authors tackled the problem of extracting and correlating information from biomedical literature on cancer cell lines, resulting in a publicly available system that automatically links genomic data with ranked, related entities and literature evidence.
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