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Published on: October 11, 2018
A pattern recognition application framework for biomedical datasets
Rodrigo Vivanco1, Aleksander B Demko, Mark Jarmasz
1National Research Coucil, Institute for Biodiagnostics, Canada. rodrigo.vivanco@nrc-cnrc.gc.ca
Summary
Scopira is an open-source C++ framework for high-performance applications, particularly in biomedical data analysis. It offers subsystems, data models, and tools for efficient development and parallel algorithms, aiding prototype migration to C++.
Area of Science:
- Computer Science
- Bioinformatics
- Software Engineering
Background:
- Developing high-performance applications requires robust frameworks with efficient data handling and parallel processing capabilities.
- Biomedical data analysis presents unique challenges demanding specialized tools for complex computations and large datasets.
Purpose of the Study:
- To introduce Scopira, a C++ programming framework designed for high-performance application development.
- To highlight Scopira's utility in biomedical data analysis and its extensibility through plug-in modules.
- To provide an open-source solution for researchers to develop and adapt efficient computational tools.
Main Methods:
- Utilizes C++ for object-oriented, efficient programming.
- Provides subsystems for memory management, serialization, GUI, and visualization.
- Supports parallel algorithm implementation using Message Passing Interface (MPI).
- Offers plug-in extensions for integrating with scripting languages like Matlab.
Main Results:
- Scopira facilitates the creation of high-performance applications, especially in biomedical data analysis.
- Enables seamless migration of prototypes to efficient C++ applications via Matlab integration.
- The framework is available as open-source, promoting research adaptation and collaboration.
- Successfully compiled for Linux and Windows XP, with Mac OS port in development.
Conclusions:
- Scopira is a versatile and efficient framework for high-performance computing in scientific research.
- Its open-source nature and extensibility foster wider adoption and development in fields like bioinformatics.
- Future development includes agent-based distributed programming for grid-computing and data mining applications.
