Investigation of machine learning techniques on proteomics: A comprehensive survey

Area of Science:

• Biochemistry
• Bioinformatics
• Computational Biology

Background:

• Proteomics involves the large-scale study of proteins, essential components of all living organisms.
• The proteome, an organism's complete protein set, dynamically changes based on cellular conditions and environmental stresses.
• Proteomics research leverages high-throughput techniques like mass spectrometry and microarrays, generating substantial datasets.

Purpose of the Study:

• To review machine learning methodologies.
• To explore the application of machine learning in the field of proteomics.
• To highlight the computational analysis of protein data.

Main Methods:

• High-throughput techniques (mass spectrometry, microarrays) for data generation.
• Bioinformatics procedures for data analysis and storage.
• Development of computational pipelines by interdisciplinary teams.

Main Results:

• Machine learning offers efficient data analysis in proteomics.
• Computational approaches significantly reduce the time required for data interpretation.
• Improved data handling and storage capabilities through bioinformatics.

Conclusions:

• Machine learning is crucial for advancing proteomics research.
• Interdisciplinary collaboration between scientists and computer experts is key.
• Computational tools enable faster and more comprehensive protein analysis.