A Comprehensive Review on Machine Learning Techniques for Protein Family Prediction

Area of Science:

• Proteomics
• Bioinformatics
• Computational Biology

Background:

• Protein family prediction is vital for understanding protein functions and evolutionary relationships.
• Existing methods face limitations in accuracy, false positive rates, and scalability.
• Current approaches often rely on sequence or structure homology, introducing biases.

Purpose of the Study:

• To conduct a comprehensive survey of machine learning (ML) techniques for protein family prediction.
• To explore and identify areas for improvement in ML methods for this task.
• To advance future research in protein family classification within proteomics.

Main Methods:

• Qualitative and quantitative analysis of existing literature on ML for protein family prediction.
• Review of various ML techniques and classifiers applied in the field.
• Identification of current trends and gaps in ML-based protein family prediction research.

Main Results:

• Multiple ML techniques and classifiers have been applied to protein family prediction.
• A significant number of studies utilize existing ML methods rather than developing novel classifiers.
• There is a clear need for the development of new and improved ML classifiers for enhanced prediction accuracy.

Conclusions:

• Machine learning offers powerful tools for protein family prediction in proteomics.
• The field requires further innovation in developing novel ML classifiers to overcome current limitations.
• Improved protein family prediction will accelerate drug discovery and genome annotation.