ArticleJournal of proteome research2009
Toward digital staining using imaging mass spectrometry and random forests.
Article in Journal of proteome research, 2009. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
32 citing papers in PubMed.
- Molecular Mapping of Atherosclerotic Plaques with MALDI Mass Spectrometry Imaging.International journal of molecular sciences · 2026Review
- Topological segmentation of mass spectrometry imaging data.Journal of mass spectrometry and advances in the clinical lab · 2025Article
- Cutaneous squamous cell carcinoma characterized by MALDI mass spectrometry imaging in combination with machine learning.Scientific reports · 2024Article
- Recent Developments and Application of Mass Spectrometry Imaging in N-Glycosylation Studies: An Overview.Mass spectrometry (Tokyo, Japan) · 2024Review
- A new deep learning framework to process Matrix-assisted Laser Desorption/Ionisation Mass Spectrometry Imaging (MALDI-MSI) data of Tissue Microarrays (TMAs).AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2023Article
- Emerging Computational Methods in Mass Spectrometry Imaging.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2022Review
- Extract Metabolomic Information from Mass Spectrometry Images Using Advanced Data Analysis.Methods in molecular biology (Clifton, N.J.) · 2022Article
- Automated biomarker candidate discovery in imaging mass spectrometry data through spatially localized Shapley additive explanations.Analytica chimica acta · 2021Article
- Mass Spectrometry Imaging for Reliable and Fast Classification of Non-Small Cell Lung Cancer Subtypes.Cancers · 2020Article
- Single-Cell Classification Using Mass Spectrometry through Interpretable Machine Learning.Analytical chemistry · 2020Article
- Spatial Metabolomics and Imaging Mass Spectrometry in the Age of Artificial Intelligence.Annual review of biomedical data science · 2020Article
- How to Apply Supervised Machine Learning Tools to MS Imaging Files: Case Study with Cancer Spheroids Undergoing Treatment with the Monoclonal Antibody Cetuximab.Journal of the American Society for Mass Spectrometry · 2020Article
- Unsupervised machine learning for exploratory data analysis in imaging mass spectrometry.Mass spectrometry reviews · 2020Review
- Mass Spectrometry Imaging Differentiates Chromophobe Renal Cell Carcinoma and Renal Oncocytoma with High Accuracy.Journal of Cancer · 2020Article
- Combining Machine Learning and Metabolomics to Identify Weight Gain Biomarkers.Frontiers in bioengineering and biotechnology · 2020Article
- Anticancer Drug Affects Metabolomic Profiles in Multicellular Spheroids: Studies Using Mass Spectrometry Imaging Combined with Machine Learning.Analytical chemistry · 2019Article
- Towards enhanced metabolomic data analysis of mass spectrometry image: Multivariate Curve Resolution and Machine Learning.Analytica chimica acta · 2018Article
- Mass Spectrometry Imaging and Integration with Other Imaging Modalities for Greater Molecular Understanding of Biological Tissues.Molecular imaging and biology · 2018Review
- Genomics of end-Pleistocene population replacement in a small mammal.Proceedings. Biological sciences · 2018Article
- Optimized Protocol To Analyze Changes in the Lipidome of Xenografts after Treatment with 2-Hydroxyoleic Acid.Analytical chemistry · 2016Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
Abstract
We show on imaging mass spectrometry (IMS) data that the Random Forest classifier can be used for automated tissue classification and that it results in predictions with high sensitivities and positive predictive values, even when intersample variability is present in the data. We further demonstrate how Markov Random Fields and vector-valued median filtering can be applied to reduce noise effects to further improve the classification results in a posthoc smoothing step. Our study gives clear evidence that digital staining by means of IMS constitutes a promising complement to chemical staining techniques.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.