Recent Developments in Machine Learning for Mass Spectrometry.

Area of Science:

• Analytical Chemistry
• Computational Biology
• Data Science

Background:

• Mass spectrometry (MS) data analysis traditionally relies on statistical and chemometric methods.
• Machine learning (ML) has seen significant advancements due to improved computational power and new algorithms, particularly in artificial neural networks (ANN) and deep learning.
• These ML advancements are increasingly integrated into various scientific disciplines.

Purpose of the Study:

• To provide a practical introduction to ML methodologies applicable to MS data.
• To review recent developments in the integration of ML with MS-based techniques.
• To offer insights into the future trajectory of ML in MS research.

Main Methods:

• Review of current ML algorithms and their application to MS.
• Discussion of practical considerations for implementing ML in MS workflows.
• Analysis of recent literature on ML in MS subdisciplines like proteomics and mass spectrometry imaging.

Main Results:

• ML methods, especially ANNs and deep learning, are enabling novel approaches for MS data analysis.
• Modern ML techniques are being widely adopted in key MS subfields.
• The integration of ML is enhancing the capabilities of MS-based applications.

Conclusions:

• ML is a rapidly evolving field with substantial potential to revolutionize MS data analysis.
• Continued research and development in ML for MS will drive innovation in areas like mass spectrometry imaging and proteomics.
• Understanding practical ML aspects is crucial for researchers utilizing MS techniques.