preon: Fast and accurate entity normalization for drug names and cancer types in precision oncology

Author:

Ermshaus Arik1,Piechotta Michael1,Rüter Gina2,Keilholz Ulrich2,Leser Ulf1,Benary Manuela23ORCID

Affiliation:

1. Institute for Computer Science, Humboldt-Universität zu Berlin , Berlin 10099, Germany

2. Charite Comprehensive Cancer Center, Charite—Universitätsmedizin Berlin , Berlin 10115, Germany

3. Core Unit Bioinformatics (CUBI), Berlin Institute of Health, Charite—Universitätsmedizin Berlin , Berlin 10115, Germany

Abstract

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/.

Funder

German Federal Ministry of Education and Research

European Fund for Regional Development

Federal State of Berlin

Innovation Committee of the Federal Joint Committee of Germany

Publisher

Oxford University Press (OUP)

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