ColocML: machine learning quantifies co-localization between mass spectrometry images | Zendy

Katja Ovchinnikova | Zendy; Lachlan Stuart | Zendy; Alexander Rakhlin | Zendy; Sergey Nikolenko | Zendy; Theodore Alexandrov | Zendy

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ColocML: machine learning quantifies co-localization between mass spectrometry images

Author(s) -

Katja Ovchinnikova,

Lachlan Stuart,

Alexander Rakhlin,

Sergey Nikolenko,

Theodore Alexandrov

Publication year - 2020

Publication title -

bioinformatics

Language(s) - Uncategorized

Resource type - Journals

SCImago Journal Rank - 3.599

H-Index - 390

eISSN - 1367-4811

pISSN - 1367-4803

DOI - 10.1093/bioinformatics/btaa085

Subject(s) - thresholding , artificial intelligence , computer science , pattern recognition (psychology) , deep learning , machine learning , data mining , image (mathematics)

Imaging mass spectrometry (imaging MS) is a prominent technique for capturing distributions of molecules in tissue sections. Various computational methods for imaging MS rely on quantifying spatial correlations between ion images, referred to as co-localization. However, no comprehensive evaluation of co-localization measures has ever been performed; this leads to arbitrary choices and hinders method development.

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