Data-driven information extraction and enrichment of molecular profiling data for cancer cell lines.

ABSTRACT:

Motivation

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 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.

Results

In this work, we present the design, implementation, and application of

SUBMITTER: Smith E 

PROVIDER: S-EPMC10978572 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature