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BioC implementations in Go, Perl, Python and Ruby.

Wanli Liu¹, Rezarta Islamaj Doğan¹, Dongseop Kwon¹

¹National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, 8600 Rockville Pike, Bethesda, MD 20894, USA, Department of Computer Engineering, Myongji University, Yongin, Republic of Korea and Institute of Computational Linguistics, University of Zurich, Zurich 8050, Switzerland.

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Summary

This summary is machine-generated.

BioC, a biomedical natural language processing data format, now supports multiple programming languages including Perl, Python, Go, and Ruby. This expansion enhances interoperability and facilitates wider adoption of text mining tools in the biomedical field.

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Area of Science:

Biomedical Informatics
Computational Linguistics
Software Engineering

Background:

Interoperability is a significant challenge for the widespread adoption of text mining and information extraction systems in the biomedical domain.
Existing BioC implementations in C++ and Java provide foundational capabilities for handling biomedical text data.
The need for broader language support to facilitate integration with diverse research environments is evident.

Purpose of the Study:

To extend the BioC (Biomedical and Clinical data) format and its associated libraries to additional programming languages.
To enhance the interoperability of biomedical text mining tools by increasing language compatibility.
To facilitate the development and deployment of natural language processing applications in the biomedical field.

Main Methods:

The BioC XML format, defined by a DTD, was extended to support Perl, Python, Go, and Ruby.
Software Wrapper and Interface Generator (SWIG) was utilized to extend the C++ implementation for Perl and one Python version.
Native data structures and libraries were employed for a second Python implementation and the Ruby implementation.
The Google language Go was also used for a BioC implementation.

Main Results:

BioC modules are now functional across multiple programming languages: C++, Java, Perl, Python, Go, and Ruby.
The extended implementations facilitate text mining tasks by enabling seamless data exchange.
These expanded BioC implementations are freely available for public use.

Conclusions:

The expansion of BioC to encompass a wider range of programming languages significantly improves its utility and accessibility.
Enhanced language support for BioC addresses the critical barrier of interoperability, promoting wider adoption of biomedical text mining tools.
The availability of BioC modules across diverse languages fosters collaborative research and accelerates advancements in biomedical natural language processing.