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

Smith, Ellery; Paloots, Rahel; Giagkos, Dimitris; Baudis, Michael; Stockinger, Kurt

doi:10.1093/bioadv/vbae045

Publication type:	Article in scientific journal
Type of review:	Peer review (publication)
Title:	Data-driven information extraction and enrichment of molecular profiling data for cancer cell lines
Authors:	Smith, Ellery Paloots, Rahel Giagkos, Dimitris Baudis, Michael Stockinger, Kurt
et. al:	No
DOI:	10.1093/bioadv/vbae045
Published in:	Bioinformatics Advances
Issue Date:	Mar-2024
Publisher / Ed. Institution:	Oxford University Press
ISSN:	2635-0041
Language:	English
Subjects:	Cancer cell line; Copy number variant; Information extraction; Natural language processing
Subject (DDC):	005: Computer programming, programs and data 006: Special computer methods
Abstract:	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. 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 concerning 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.
URI:	https://digitalcollection.zhaw.ch/handle/11475/30347
Related research data:	https://github.com/progenetix/cancercelllines-web
Fulltext version:	Accepted version
License (according to publishing contract):	Licence according to publishing contract
Departement:	School of Engineering
Organisational Unit:	Institute of Computer Science (InIT)
Published as part of the ZHAW project:	INODE – Intelligent Open Data Exploration (EU Horizon 2020)
Appears in collections:	Publikationen School of Engineering

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Smith, E., Paloots, R., Giagkos, D., Baudis, M., & Stockinger, K. (2024). Data-driven information extraction and enrichment of molecular profiling data for cancer cell lines. Bioinformatics Advances. https://doi.org/10.1093/bioadv/vbae045

Smith, E. et al. (2024) ‘Data-driven information extraction and enrichment of molecular profiling data for cancer cell lines’, Bioinformatics Advances [Preprint]. Available at: https://doi.org/10.1093/bioadv/vbae045.

E. Smith, R. Paloots, D. Giagkos, M. Baudis, and K. Stockinger, “Data-driven information extraction and enrichment of molecular profiling data for cancer cell lines,” Bioinformatics Advances, Mar. 2024, doi: 10.1093/bioadv/vbae045.

SMITH, Ellery, Rahel PALOOTS, Dimitris GIAGKOS, Michael BAUDIS und Kurt STOCKINGER, 2024. Data-driven information extraction and enrichment of molecular profiling data for cancer cell lines. Bioinformatics Advances. März 2024. DOI 10.1093/bioadv/vbae045

Smith, Ellery, Rahel Paloots, Dimitris Giagkos, Michael Baudis, and Kurt Stockinger. 2024. “Data-Driven Information Extraction and Enrichment of Molecular Profiling Data for Cancer Cell Lines.” Bioinformatics Advances, March. https://doi.org/10.1093/bioadv/vbae045.

Smith, Ellery, et al. “Data-Driven Information Extraction and Enrichment of Molecular Profiling Data for Cancer Cell Lines.” Bioinformatics Advances, Mar. 2024, https://doi.org/10.1093/bioadv/vbae045.