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Machine learning in bioinformatics: a brief survey and recommendations for practitioners
Harish Bhaskar1, David C Hoyle, Sameer Singh
1School of Engineering, Computer Science & Mathematics, University of Exeter, Exeter EX4 4QF, UK. h.bhaskar@exeter.ac.uk
Computers in Biology and Medicine
|October 18, 2005
Summary
This study reviews machine learning (ML) applications in bioinformatics, focusing on feature and model selection. It assesses how well established pattern recognition practices are adopted in bioinformatics research and offers recommendations for proper ML use.
Area of Science:
- Bioinformatics
- Machine Learning
- Pattern Recognition
Background:
- Machine learning (ML) is extensively applied in bioinformatics.
- Significant experience exists in applying ML techniques in pattern recognition.
- The current study evaluates the integration of this experience into bioinformatics.
Purpose of the Study:
- To identify key issues in applying ML tools in bioinformatics.
- To focus on general aspects of feature and model parameter selection.
- To assess the adoption of pattern recognition best practices in bioinformatics studies.
Main Methods:
- Review of published bioinformatics studies in leading journals over the last 5 years.
- Analysis of feature and model parameter selection strategies used in these studies.
- Comparison of adopted practices against established principles from pattern recognition research.
Main Results:
- Identified critical issues in the application of ML tools in bioinformatics.
- Assessed the extent to which bioinformatics studies leverage existing ML expertise.
- Highlighted areas where bioinformatics research can improve ML implementation.
Conclusions:
- Bioinformatics research can benefit from more rigorous application of ML principles.
- Recommendations are provided for the proper use of ML techniques in bioinformatics.
- Emphasizes the need to integrate established machine learning practices into bioinformatics.