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Gina Rüter

dblp:371/1244 · DBLP profile ↗
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1ranked-venue papers
0as first author
1since 2021 · last 2024
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Applied, interdisciplinary, general and emerging computing · 1 · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Interdisciplinary, comprehensive, and emerging computing
1 paper
Bioinformatics and computational biology · 56% Medical and health informatics · 44%

Topics — the 2 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Bioinformatics and computational biology › biomedical text mining
entity normalization
0.812024
preon: Fast and accurate entity normalization for drug names and cancer types in precision oncology · Bioinform. 2024
Medical and health informatics › precision medicine
precision oncology
0.812024
preon: Fast and accurate entity normalization for drug names and cancer types in precision oncology · Bioinform. 2024

Methods — techniques the papers use, named apart from their topics

string matching · 0.8
YearPublicationVenuePosition
2024 preon: Fast and accurate entity normalization for drug names and cancer types in precision oncology
abstract
MOTIVATION: In precision oncology (PO), clinicians aim to find the best treatment for any patient based on their molecular characterization. A major bottleneck is the manual annotation and evaluation of individual variants, for which usually a range of knowledge bases are screened. To incorporate and integrate the vast information of different databases, fast and accurate methods for harmonizing databases with different types of information are necessary. An essential step for harmonization in PO includes the normalization of tumor entities as well as therapy options for patients. SUMMARY: preon is a fast and accurate library for the normalization of drug names and cancer types in large-scale data integration. AVAILABILITY AND IMPLEMENTATION: preon is implemented in Python and freely available via the PyPI repository. Source code and the data underlying this article are available in GitHub at https://github.com/ermshaua/preon/.
Arik Ermshaus, Michael Piechotta, Gina Rüter, Ulrich Keilholz, Ulf Leser, Manuela Benary
Bioinform.3