Nico Verbeeck, PhD

Co-founder & COO

Nico has been working on MSI data analysis for his entire professional career, obtaining his PhD on the topic at KU Leuven. In addition to the application of machine learning in MSI, his expertise includes computer vision and methods for image registration.

Content authored by Nico

Non-negative matrix factorization in MSI

Published on March 13, 2021
Read time: 5 minutes
Factorization of MSI data - part 1

Published on June 8, 2020
Read time: 11 minutes

Publications

Nico is a co-author of the following publications:

Zhang et al., 2022. Incorporating morphology via deep learning improves classification performance of MALDI imaging for skin lesions
Verbeeck et al., 2022. Classification of cirrhotic patient samples based on imaging MS of multiplexed N-glycan markers in biofluids., 70th ASMS Conference on Mass Spectrometry and Allied Topics, Minneapolis, Minnesota, USA
Verbeeck et al., 2021. Annotation Studio: a web portal to support the development of IMS-based diagnostic assays, 70th ASMS Conference on Mass Spectrometry and Allied Topics, Minneapolis, Minnesota, USA
Al-Rohil et al., 2021. Diagnosis of melanoma by imaging mass spectrometry: Development and validation of a melanoma prediction model, Journal of Cutaneous Pathology 12:1455–1462
Zhang et al., 2021. Spatially-Aware Clustering of Ion Images in Mass Spectrometry Imaging Data Using Deep Learning, Analytical and Bioanalytical Chemistry 413:2803–2819
Moerman et al., 2020. Metabolite Explorer: A Software Tool for Targeted Analysis of Mass Spectrometry Imaging Data, ASMS 2020 Reboot, online
Zhang et al., 2020. Spatially-Aware Clustering of Ion Images in Mass Spectrometry Imaging Data Through the Use of Pre-trained Neural Networks, ASMS 2020 Reboot, online
Kobarg et al., 2019. Rapid Bioinformatics Prototyping Based on a Public Application Programming Interface, Ourcon VII, Saint-Malo, France
Verbeeck et al., 2020. Unsupervised Machine Learning for Exploratory Data Analysis in Imaging Mass Spectrometry, Mass Spectrometry Reviews 39:245–291
Smets et al., 2019. Evaluation of Distance Metrics and Spatial Autocorrelation in Uniform Manifold Approximation and Projection Applied to Mass Spectrometry Imaging Data, Analytical Chemistry 91:5706–5714

Nico Verbeeck, PhD

Co-founder & COO

Nico has been working on MSI data analysis for his entire professional career, obtaining his PhD on the topic at KU Leuven. In addition to the application of machine learning in MSI, his expertise includes computer vision and methods for image registration.

Content authored by Nico

Non-negative matrix factorization in MSI

Factorization of MSI data - part 1

Publications

Nico is a co-author of the following publications:

Zhang et al., 2022. Incorporating morphology via deep learning improves classification performance of MALDI imaging for skin lesions

Verbeeck et al., 2022. Classification of cirrhotic patient samples based on imaging MS of multiplexed N-glycan markers in biofluids., 70th ASMS Conference on Mass Spectrometry and Allied Topics, Minneapolis, Minnesota, USA

Verbeeck et al., 2021. Annotation Studio: a web portal to support the development of IMS-based diagnostic assays, 70th ASMS Conference on Mass Spectrometry and Allied Topics, Minneapolis, Minnesota, USA

Al-Rohil et al., 2021. Diagnosis of melanoma by imaging mass spectrometry: Development and validation of a melanoma prediction model, Journal of Cutaneous Pathology 12:1455–1462

Zhang et al., 2021. Spatially-Aware Clustering of Ion Images in Mass Spectrometry Imaging Data Using Deep Learning, Analytical and Bioanalytical Chemistry 413:2803–2819

Moerman et al., 2020. Metabolite Explorer: A Software Tool for Targeted Analysis of Mass Spectrometry Imaging Data, ASMS 2020 Reboot, online

Zhang et al., 2020. Spatially-Aware Clustering of Ion Images in Mass Spectrometry Imaging Data Through the Use of Pre-trained Neural Networks, ASMS 2020 Reboot, online

Kobarg et al., 2019. Rapid Bioinformatics Prototyping Based on a Public Application Programming Interface, Ourcon VII, Saint-Malo, France

Verbeeck et al., 2020. Unsupervised Machine Learning for Exploratory Data Analysis in Imaging Mass Spectrometry, Mass Spectrometry Reviews 39:245–291

Smets et al., 2019. Evaluation of Distance Metrics and Spatial Autocorrelation in Uniform Manifold Approximation and Projection Applied to Mass Spectrometry Imaging Data, Analytical Chemistry 91:5706–5714