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Expert Rev Proteomics. 2016 Jul;13(7):685-96. doi: 10.1080/14789450.2016.1200470. Epub 2016 Jun 23.

Machine learning approaches in MALDI-MSI: clinical applications.

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a Department of Medicine and Surgery , University of Milano Bicocca , Monza Brianza , Italy.
b Department of Informatics, Systems and Communication , University of Milano Bicocca , Milano , Italy.



Despite the unquestionable advantages of Matrix-Assisted Laser Desorption/Ionization Mass Spectrometry Imaging in visualizing the spatial distribution and the relative abundance of biomolecules directly on-tissue, the yielded data is complex and high dimensional. Therefore, analysis and interpretation of this huge amount of information is mathematically, statistically and computationally challenging.


This article reviews some of the challenges in data elaboration with particular emphasis on machine learning techniques employed in clinical applications, and can be useful in general as an entry point for those who want to study the computational aspects. Several characteristics of data processing are described, enlightening advantages and disadvantages. Different approaches for data elaboration focused on clinical applications are also provided. Practical tutorial based upon Orange Canvas and Weka software is included, helping familiarization with the data processing. Expert commentary: Recently, MALDI-MSI has gained considerable attention and has been employed for research and diagnostic purposes, with successful results. Data dimensionality constitutes an important issue and statistical methods for information-preserving data reduction represent one of the most challenging aspects. The most common data reduction methods are characterized by collecting independent observations into a single table. However, the incorporation of relational information can improve the discriminatory capability of the data.


MALDI; Mass spectrometry imaging; classification; clustering; feature selection; machine learning

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