BioC implementations in Go, Perl, Python and Ruby
Open accessAbstract
As part of a communitywide effort for evaluating text mining and information extraction systems applied to the biomedical domain, BioC is focused on the goal of interoperability, currently a major barrier to wide-scale adoption of text mining tools. BioC is a simple XML format, specified by DTD, for exchanging data for biomedical natural language processing. With initial implementations in C++ and Java, BioC provides libraries of code for reading and writing BioC text documents and annotations. We extend BioC to Perl, Python, Go and Ruby. We used SWIG to extend the C++ implementation for Perl and one Python implementation. A second Python implementation and the Ruby implementation use native data structures and libraries. BioC is also implemented in the Google language Go. BioC modules are functional in all of these languages, which can facilitate text mining tasks. BioC implementations are freely available through the BioC site: http://bioc.sourceforge.net. Database URL: http://bioc.sourceforge.net/
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Perl excels at regular expressions and complex pattern matching, making it ideal for parsing logs, transforming data, and automating tasks involving large volumes of text.
“Perl, often described as the”
From Modern Approaches to Unix Automation: Shell Scripting, Configuration Management, and Security (2025) · cites this paper as Liu et al., 2014
The Introduction (page not provided) describes Perl as a scripting language used in bioinformatics and natural-language processing, and notes BioPerl’s use in parsing BLAST output. The paper does not address Perl’s proficiency with regular expressions or complex pattern matching, or its suitability for parsing logs and automating work on large volumes of text. (LLM, conf=0.83)
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Research works printed but not linked: 8
- [1]Comeau D.C. Islamaj Doğan R. Ciccarese P.. (2013) BioC: a minimalist approach to interoperability for biomedical text processing. Database (Oxford), 2013, bat064 24048470 PMC3889917
- [2]Stajich J.E. Block D. Boulez K.. (2002) The Bioperl toolkit: Perl modules for the life sciences. Genome Res., 12, 1611– 1618 12368254 PMC187536
- [3]Cock P.J. Antao T. Chang J.T.. (2009) Biopython: freely available Python tools for computational molecular biology and bioinformatics. Bioinformatics, 25, 1422– 1423 19304878 PMC2682512
- [4]Goto N. Prins P. Nakao M. (2010) BioRuby: bioinformatics software for the Ruby programming language. Bioinformatics, 26, 2617– 2619 20739307 PMC2951089
- [5]Bonnal R.J. Aerts J. Githinji G.. (2012) Biogem: an effective tool based approach for scaling up open source software development in bioinformatics. Bioinformatics, 28, 1035– 1037 22332238 PMC3315718
- [6]Yeganova L. Comeau D.C. Wilbur W.J. (2011) Machine learning with naturally labeled data for identifying abbreviation definitions. BMC Bioinformatics, 12 (Suppl. 3), S6 21658293 PMC3111592
- [7]Islamaj Dogan R. Comeau D.C. Yeganova L. (2014) Finding abbreviations in biomedical literature: three BioC-compatible modules and four BioC formatted corpora. Database, doi: 10.1093/database/bau044 PMC4051513 24914232 · DOI
- [8]Kwon D. Kim S. Shin S.Y. Wilbur W.J. (2013) BioQRator: a web-based interactive biomedical literature curating system. In: Fourth BioCreative Challenge Workshop, pp. 241– 246
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Modern Approaches to Unix Automation: Shell Scripting, Configuration Management, and Security
Madamanchi, S. R. (2025) · International Journal for Research in Applied Science and Engineering Technology · doi:10.22214/ijraset.2025.72773 · Cited by 2 · see citing works
- Authors
- Liu, W., Islamaj Do an, R., Kwon, D., Marques, H., Rinaldi, F., Wilbur, W. J., & Comeau, D. C.
- Affiliations
- National Institutes of Health (US)
- Corresponding author
- Comeau, D. C.
- ORCID
- Venue
- Database
- Publisher
- Oxford University Press (OUP)
- Published
- 2014-06-23
- Volume
- 2014
- Issue
- 0
- Pages
- bau059-bau059
- ISSN
- 1758-0463 (ISSN-L)
- PMC
- PMC4067548
- OpenAlex
- W2098068971
- Cited by
- 10
- References
- 0
- Citations by year
- 2025: 1 · 2023: 2 · 2021: 1 · 2020: 1 · 2019: 1 · 2016: 2 · 2014: 2
- FWCI
- 0.55
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- public-domain
- Locations
- Database (publishedVersion, public-domain)
- PubMed Central (publishedVersion, public-domain)
- PubMed (publishedVersion)
- Zurich Open Repository and Archive (University of Zurich) (acceptedVersion, other-oa)
- Zurich Open Repository and Archive (University of Zurich) (submittedVersion, other-oa)
- Zurich Open Repository and Archive (University of Zurich)
- Funders
- National Science Foundation
- National Research Foundation
- Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung (118396, 105315, 100014, 130558, 100014-118396/1)
- National Research Foundation of Korea
- Universität Zürich
- National Institutes of Health
- U.S. National Library of Medicine
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Biomedical Text Mining and Ontologies
- Social Sciences › Decision Sciences › Information Systems and Management › Scientific Computing and Data Management
- Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
- Keywords
- BioC, biomedical text mining, information extraction, interoperability, XML data exchange, Go, Perl, Python, ruby, SWIG, biomedical natural language processing, software implementation
- MeSH terms
- Algorithms, Programming Languages, User-Computer Interface, Databases as Topic, Data Mining
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