Supporting Data from: Resolution-Adaptive Binning Enhances Machine Learning Modeling by Interbatch and Multiplatform Orbitrap-Based Shotgun Mass Spectrometry Data Integration
Authors/Creators
-
1.
Hong Kong Baptist University
-
2.
University of Hong Kong
-
3.
Hong Kong University of Science and Technology
- 4. University of Manchester
- 5. Yale University
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6.
Eastern Institute of Technology, Ningbo
Contributors
Researcher (6):
Supervisor (4):
-
1.
Hong Kong Baptist University
-
2.
University of Hong Kong
- 3. Hong Kong University of Science and Technology
- 4. University of Manchester
- 5. Yale University
-
6.
Eastern Institute of Technology, Ningbo
Description
§ Supporting file 1
A spreadsheet recording numerous iterations for hyperparameter tuning for logistic regression model.
§ Supporting file 2
A spreadsheet recording numerous iterations for hyperparameter tuning for linear Support Vector Classifier model.
§ Supporting file 3
A spreadsheet recording numerous iterations for hyperparameter tuning for gradient boosting model.
§ Supporting file 4
A spreadsheet recording numerous iterations for hyperparameter tuning for eXtreme Gradient Boosting model.
§ Supporting file 5
A spreadsheet recording numerous iterations for hyperparameter tuning for decision tree model.
§ Supporting file 6
A spreadsheet recording numerous iterations for hyperparameter tuning for random forest model.
§ Supporting file 7
A spreadsheet recording binning performance before data integration (batch 1).
§ Supporting file 8
A spreadsheet recording binning performance before data integration (batch 2).
§ Supporting file 9
A spreadsheet recording binning performance after inter-batch data integration.
§ Supporting file 10
A spreadsheet recording binning performance for the recovery analysis.
§ Supporting dataset 1
A zip collection of DESI-MSI raw data for the trainging dataset.
§ Supporting dataset 2
A zip collection of DESI-MSI raw data for the external dataset used for data ingestion.
§ Supporting dataset 3
A zip collection of MS raw data for the fine-needle aspiration smear DESI-MSI dataset.
§ Supporting dataset 4
A zip collection of MS raw data for the direction infusion dataset.
§ Supporting dataset 5
A zip collection of histograms of the m/z buckets before data integration (batch 1).
§ Supporting dataset 6
A zip collection of histograms of the m/z buckets before data integration (batch 2).
§ Supporting dataset 7
A zip collection of histograms of the m/z buckets after inter-batch data integration.
§ Supporting dataset 8
A zip collection of histograms of the m/z buckets for the recovery analysis.
Files
Supporting file 7.csv
Files
(2.1 GB)
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md5:fd6038092e2c2ac889aec99084d0b860
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md5:b370f720fee72cc4bf8e31b522ba65da
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md5:11f5e1be6a883aa494c474ee0dee8791
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md5:42d2742117f067c0d0144cd2f2b1fae5
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md5:4246a2c43320e036667738c06b5461bf
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Additional details
Related works
- Is supplement to
- Journal article: 10.1021/acs.analchem.5c05874 (DOI)
References
- raMSIn