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folder_walk.py
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masses.py
masses.py
matrixwithdict.py
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mgf_processing.py
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model_combine.py
model_combine.py
model_gly.py
model_gly.py
mz_all.py
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postprocessing_spectra.py
postprocessing_spectra.py
preprocess.py
preprocess.py
requirements.txt
requirements.txt
utils.py
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Repository files navigation

DeepGP

DeepGP is a deep learning framework for the prediction of MS/MS spectra and retention time of glycopeptides.

Tutorial

User Guide

For detailed step-by-step instructions on how to get started with DeepGP, please refer to User_guide.md available in the main folder.

What's Inside User Guide

Package Requirements: A list of all required packages and software.

Package Installation: Step-by-step guide to installing necessary packages.

Demo Data: Information and access to demo data sets.

Demo Data Description: Detailed description of the demo data.

Step-by-Step Instructions: Comprehensive guide to help you run and understand DeepGP.

Model

The model is available at the Google Drive.

Here are the trained DeepGP models. These models are organized into five files, each denoted by the following names: DeepFLR, human, mouse, human&mouse and mouse_rt.

DeepFLR: This is the base model.

mouse: This is the DeepGP model for spectra prediction trained with mouse datasets, built on top of the DeepFLR base model.

human: This is the DeepGP model for spectra prediction trained with human datasets, built on top of the DeepFLR base model.

human&mouse: This is the DeepGP model for spectra prediction trained with both human and mouse datasets, built on top of the DeepFLR base model.

mouse_rt: The is the DeepGP model for retention time prediction trained with mouse datasets, built on top of the DeepFLR base model.

For further details, please refer to the User Guide.

Demo data 2

The demo data for iRT prediction is available at the Google Drive.

This demo data is for clear and comprehensive presentation of iRT pre-processing, calibration, model training, and prediction. It includes three relatively large mouse datasets.

For further details, please refer to the User Guide.

Post analysis

We also present the post-analysis code for the re-identification in the main folder/Post_analysis. For detailed step-by-step instructions on how to perform post analysis, please refer to User_guide_post_analysis.docx available in the main main folder/Post_analysis.

The demo data for post analysis is available at the Google Drive.

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2_train_byBY.py
2_train_byBY.py
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AA_vocab
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Bertmodel.py
Bertmodel.py
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LICENSE
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folder_walk.py
folder_walk.py
masses.py
masses.py
matrixwithdict.py
matrixwithdict.py
mgf_processing.py
mgf_processing.py
model_combine.py
model_combine.py
model_gly.py
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mz_all.py
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postprocessing_spectra.py
postprocessing_spectra.py
preprocess.py
preprocess.py
requirements.txt
requirements.txt
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Repository files navigation
DeepGP
DeepGP is a deep learning framework for the prediction of MS/MS spectra and retention time of glycopeptides.
Tutorial
User Guide
For detailed step-by-step instructions on how to get started with DeepGP, please refer to User_guide.md available in the main folder.
What's Inside User Guide
Package Requirements: A list of all required packages and software.
Package Installation: Step-by-step guide to installing necessary packages.
Demo Data: Information and access to demo data sets.
Demo Data Description: Detailed description of the demo data.
Step-by-Step Instructions: Comprehensive guide to help you run and understand DeepGP.
Model
The model is available at the Google Drive.
Here are the trained DeepGP models. These models are organized into five files, each denoted by the following names: DeepFLR, human, mouse, human&mouse and mouse_rt.
DeepFLR: This is the base model.
mouse: This is the DeepGP model for spectra prediction trained with mouse datasets, built on top of the DeepFLR base model.
human: This is the DeepGP model for spectra prediction trained with human datasets, built on top of the DeepFLR base model.
human&mouse: This is the DeepGP model for spectra prediction trained with both human and mouse datasets, built on top of the DeepFLR base model.
mouse_rt: The is the DeepGP model for retention time prediction trained with mouse datasets, built on top of the DeepFLR base model.
For further details, please refer to the User Guide.
Demo data 2
The demo data for iRT prediction is available at the Google Drive.
This demo data is for clear and comprehensive presentation of iRT pre-processing, calibration, model training, and prediction. It includes three relatively large mouse datasets.
For further details, please refer to the User Guide.
Post analysis
We also present the post-analysis code for the re-identification in the main folder/Post_analysis. For detailed step-by-step instructions on how to perform post analysis, please refer to User_guide_post_analysis.docx available in the main main folder/Post_analysis.
The demo data for post analysis is available at the Google Drive.
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2_train_byBY.py
2_train_byBY.py
2_train_rt.py
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3_replace_predict_byBY.py
3_replace_predict_byBY.py
3_replace_predict_rt.py
3_replace_predict_rt.py
AA_vocab
AA_vocab
Bertmodel.py
Bertmodel.py
Bertmodel_BY.py
Bertmodel_BY.py
GNN_edge_regression.py
GNN_edge_regression.py
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GNN_global_representation.py
LICENSE
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MSELoss_for_byBY.py
MSELoss_for_byBY.py
README.md
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User_guide.md
User_guide.md
folder_walk.py
folder_walk.py
masses.py
masses.py
matrixwithdict.py
matrixwithdict.py
mgf_processing.py
mgf_processing.py
model_combine.py
model_combine.py
model_gly.py
model_gly.py
mz_all.py
mz_all.py
postprocessing_spectra.py
postprocessing_spectra.py
preprocess.py
preprocess.py
requirements.txt
requirements.txt
utils.py
utils.py
Repository files navigation
DeepGP
DeepGP is a deep learning framework for the prediction of MS/MS spectra and retention time of glycopeptides.
Tutorial
User Guide
For detailed step-by-step instructions on how to get started with DeepGP, please refer to User_guide.md available in the main folder.
What's Inside User Guide
Package Requirements: A list of all required packages and software.
Package Installation: Step-by-step guide to installing necessary packages.
Demo Data: Information and access to demo data sets.
Demo Data Description: Detailed description of the demo data.
Step-by-Step Instructions: Comprehensive guide to help you run and understand DeepGP.
Model
The model is available at the Google Drive.
Here are the trained DeepGP models. These models are organized into five files, each denoted by the following names: DeepFLR, human, mouse, human&mouse and mouse_rt.
DeepFLR: This is the base model.
mouse: This is the DeepGP model for spectra prediction trained with mouse datasets, built on top of the DeepFLR base model.
human: This is the DeepGP model for spectra prediction trained with human datasets, built on top of the DeepFLR base model.
human&mouse: This is the DeepGP model for spectra prediction trained with both human and mouse datasets, built on top of the DeepFLR base model.
mouse_rt: The is the DeepGP model for retention time prediction trained with mouse datasets, built on top of the DeepFLR base model.
For further details, please refer to the User Guide.
Demo data 2
The demo data for iRT prediction is available at the Google Drive.
This demo data is for clear and comprehensive presentation of iRT pre-processing, calibration, model training, and prediction. It includes three relatively large mouse datasets.
For further details, please refer to the User Guide.
Post analysis
We also present the post-analysis code for the re-identification in the main folder/Post_analysis. For detailed step-by-step instructions on how to perform post analysis, please refer to User_guide_post_analysis.docx available in the main main folder/Post_analysis.
The demo data for post analysis is available at the Google Drive.
About
No description, website, or topics provided.
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11 stars
Watchers
2 watching
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1 fork
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2_train_byBY.py
2_train_byBY.py
2_train_rt.py
2_train_rt.py
3_replace_predict_byBY.py
3_replace_predict_byBY.py
3_replace_predict_rt.py
3_replace_predict_rt.py
AA_vocab
AA_vocab
Bertmodel.py
Bertmodel.py
Bertmodel_BY.py
Bertmodel_BY.py
GNN_edge_regression.py
GNN_edge_regression.py
GNN_global_representation.py
GNN_global_representation.py
LICENSE
LICENSE
MSELoss_for_byBY.py
MSELoss_for_byBY.py
README.md
README.md
User_guide.md
User_guide.md
folder_walk.py
folder_walk.py
masses.py
masses.py
matrixwithdict.py
matrixwithdict.py
mgf_processing.py
mgf_processing.py
model_combine.py
model_combine.py
model_gly.py
model_gly.py
mz_all.py
mz_all.py
postprocessing_spectra.py
postprocessing_spectra.py
preprocess.py
preprocess.py
requirements.txt
requirements.txt
utils.py
utils.py
Repository files navigation
DeepGP
DeepGP is a deep learning framework for the prediction of MS/MS spectra and retention time of glycopeptides.
Tutorial
User Guide
For detailed step-by-step instructions on how to get started with DeepGP, please refer to User_guide.md available in the main folder.
What's Inside User Guide
Package Requirements: A list of all required packages and software.
Package Installation: Step-by-step guide to installing necessary packages.
Demo Data: Information and access to demo data sets.
Demo Data Description: Detailed description of the demo data.
Step-by-Step Instructions: Comprehensive guide to help you run and understand DeepGP.
Model
The model is available at the Google Drive.
Here are the trained DeepGP models. These models are organized into five files, each denoted by the following names: DeepFLR, human, mouse, human&mouse and mouse_rt.
DeepFLR: This is the base model.
mouse: This is the DeepGP model for spectra prediction trained with mouse datasets, built on top of the DeepFLR base model.
human: This is the DeepGP model for spectra prediction trained with human datasets, built on top of the DeepFLR base model.
human&mouse: This is the DeepGP model for spectra prediction trained with both human and mouse datasets, built on top of the DeepFLR base model.
mouse_rt: The is the DeepGP model for retention time prediction trained with mouse datasets, built on top of the DeepFLR base model.
For further details, please refer to the User Guide.
Demo data 2
The demo data for iRT prediction is available at the Google Drive.
This demo data is for clear and comprehensive presentation of iRT pre-processing, calibration, model training, and prediction. It includes three relatively large mouse datasets.
For further details, please refer to the User Guide.
Post analysis
We also present the post-analysis code for the re-identification in the main folder/Post_analysis. For detailed step-by-step instructions on how to perform post analysis, please refer to User_guide_post_analysis.docx available in the main main folder/Post_analysis.
The demo data for post analysis is available at the Google Drive.
About
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2_train_byBY.py
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2_train_rt.py
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3_replace_predict_byBY.py
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3_replace_predict_rt.py
3_replace_predict_rt.py
AA_vocab
AA_vocab
Bertmodel.py
Bertmodel.py
Bertmodel_BY.py
Bertmodel_BY.py
GNN_edge_regression.py
GNN_edge_regression.py
GNN_global_representation.py
GNN_global_representation.py
LICENSE
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MSELoss_for_byBY.py
MSELoss_for_byBY.py
README.md
README.md
User_guide.md
User_guide.md
folder_walk.py
folder_walk.py
masses.py
masses.py
matrixwithdict.py
matrixwithdict.py
mgf_processing.py
mgf_processing.py
model_combine.py
model_combine.py
model_gly.py
model_gly.py
mz_all.py
mz_all.py
postprocessing_spectra.py
postprocessing_spectra.py
preprocess.py
preprocess.py
requirements.txt
requirements.txt
utils.py
utils.py
Repository files navigation
DeepGP
DeepGP is a deep learning framework for the prediction of MS/MS spectra and retention time of glycopeptides.
Tutorial
User Guide
For detailed step-by-step instructions on how to get started with DeepGP, please refer to User_guide.md available in the main folder.
What's Inside User Guide
Package Requirements: A list of all required packages and software.
Package Installation: Step-by-step guide to installing necessary packages.
Demo Data: Information and access to demo data sets.
Demo Data Description: Detailed description of the demo data.
Step-by-Step Instructions: Comprehensive guide to help you run and understand DeepGP.
Model
The model is available at the Google Drive.
Here are the trained DeepGP models. These models are organized into five files, each denoted by the following names: DeepFLR, human, mouse, human&mouse and mouse_rt.
DeepFLR: This is the base model.
mouse: This is the DeepGP model for spectra prediction trained with mouse datasets, built on top of the DeepFLR base model.
human: This is the DeepGP model for spectra prediction trained with human datasets, built on top of the DeepFLR base model.
human&mouse: This is the DeepGP model for spectra prediction trained with both human and mouse datasets, built on top of the DeepFLR base model.
mouse_rt: The is the DeepGP model for retention time prediction trained with mouse datasets, built on top of the DeepFLR base model.
For further details, please refer to the User Guide.
Demo data 2
The demo data for iRT prediction is available at the Google Drive.
This demo data is for clear and comprehensive presentation of iRT pre-processing, calibration, model training, and prediction. It includes three relatively large mouse datasets.
For further details, please refer to the User Guide.
Post analysis
We also present the post-analysis code for the re-identification in the main folder/Post_analysis. For detailed step-by-step instructions on how to perform post analysis, please refer to User_guide_post_analysis.docx available in the main main folder/Post_analysis.
The demo data for post analysis is available at the Google Drive.
About
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1 fork
Report repository
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injectUserscript("// Auto-enable theater mode on YouTube\n(function() {\n function tryTheater() {\n var btn = document.querySelector('button[aria-label=\"Theater mode\"], ytd-player #player button[title=\"Theater mode\"]');\n if (btn && !btn.classList.contains('activated')) {\n btn.click();\n }\n }\n \n // Try immediately\n tryTheater();\n \n // Try after navigation (SPA)\n var lastUrl = location.href;\n setInterval(function() {\n if (location.href !== lastUrl) {\n lastUrl = location.href;\n setTimeout(tryTheater, 500);\n }\n }, 1000);\n \n // Also try on player load\n var observer = new MutationObserver(tryTheater);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "YouTube Theater Mode Default");
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2_train_byBY.py
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3_replace_predict_rt.py
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AA_vocab
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Bertmodel.py
Bertmodel.py
Bertmodel_BY.py
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GNN_edge_regression.py
GNN_edge_regression.py
GNN_global_representation.py
GNN_global_representation.py
LICENSE
LICENSE
MSELoss_for_byBY.py
MSELoss_for_byBY.py
README.md
README.md
User_guide.md
User_guide.md
folder_walk.py
folder_walk.py
masses.py
masses.py
matrixwithdict.py
matrixwithdict.py
mgf_processing.py
mgf_processing.py
model_combine.py
model_combine.py
model_gly.py
model_gly.py
mz_all.py
mz_all.py
postprocessing_spectra.py
postprocessing_spectra.py
preprocess.py
preprocess.py
requirements.txt
requirements.txt
utils.py
utils.py
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DeepGP
DeepGP is a deep learning framework for the prediction of MS/MS spectra and retention time of glycopeptides.
Tutorial
User Guide
For detailed step-by-step instructions on how to get started with DeepGP, please refer to User_guide.md available in the main folder.
What's Inside User Guide
Package Requirements: A list of all required packages and software.
Package Installation: Step-by-step guide to installing necessary packages.
Demo Data: Information and access to demo data sets.
Demo Data Description: Detailed description of the demo data.
Step-by-Step Instructions: Comprehensive guide to help you run and understand DeepGP.
Model
The model is available at the Google Drive.
Here are the trained DeepGP models. These models are organized into five files, each denoted by the following names: DeepFLR, human, mouse, human&mouse and mouse_rt.
DeepFLR: This is the base model.
mouse: This is the DeepGP model for spectra prediction trained with mouse datasets, built on top of the DeepFLR base model.
human: This is the DeepGP model for spectra prediction trained with human datasets, built on top of the DeepFLR base model.
human&mouse: This is the DeepGP model for spectra prediction trained with both human and mouse datasets, built on top of the DeepFLR base model.
mouse_rt: The is the DeepGP model for retention time prediction trained with mouse datasets, built on top of the DeepFLR base model.
For further details, please refer to the User Guide.
Demo data 2
The demo data for iRT prediction is available at the Google Drive.
This demo data is for clear and comprehensive presentation of iRT pre-processing, calibration, model training, and prediction. It includes three relatively large mouse datasets.
For further details, please refer to the User Guide.
Post analysis
We also present the post-analysis code for the re-identification in the main folder/Post_analysis. For detailed step-by-step instructions on how to perform post analysis, please refer to User_guide_post_analysis.docx available in the main main folder/Post_analysis.
The demo data for post analysis is available at the Google Drive.
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1_rt_calibration.py
2_train_byBY.py
2_train_byBY.py
2_train_rt.py
2_train_rt.py
3_replace_predict_byBY.py
3_replace_predict_byBY.py
3_replace_predict_rt.py
3_replace_predict_rt.py
AA_vocab
AA_vocab
Bertmodel.py
Bertmodel.py
Bertmodel_BY.py
Bertmodel_BY.py
GNN_edge_regression.py
GNN_edge_regression.py
GNN_global_representation.py
GNN_global_representation.py
LICENSE
LICENSE
MSELoss_for_byBY.py
MSELoss_for_byBY.py
README.md
README.md
User_guide.md
User_guide.md
folder_walk.py
folder_walk.py
masses.py
masses.py
matrixwithdict.py
matrixwithdict.py
mgf_processing.py
mgf_processing.py
model_combine.py
model_combine.py
model_gly.py
model_gly.py
mz_all.py
mz_all.py
postprocessing_spectra.py
postprocessing_spectra.py
preprocess.py
preprocess.py
requirements.txt
requirements.txt
utils.py
utils.py
Repository files navigation
DeepGP
DeepGP is a deep learning framework for the prediction of MS/MS spectra and retention time of glycopeptides.
Tutorial
User Guide
For detailed step-by-step instructions on how to get started with DeepGP, please refer to User_guide.md available in the main folder.
What's Inside User Guide
Package Requirements: A list of all required packages and software.
Package Installation: Step-by-step guide to installing necessary packages.
Demo Data: Information and access to demo data sets.
Demo Data Description: Detailed description of the demo data.
Step-by-Step Instructions: Comprehensive guide to help you run and understand DeepGP.
Model
The model is available at the Google Drive.
Here are the trained DeepGP models. These models are organized into five files, each denoted by the following names: DeepFLR, human, mouse, human&mouse and mouse_rt.
DeepFLR: This is the base model.
mouse: This is the DeepGP model for spectra prediction trained with mouse datasets, built on top of the DeepFLR base model.
human: This is the DeepGP model for spectra prediction trained with human datasets, built on top of the DeepFLR base model.
human&mouse: This is the DeepGP model for spectra prediction trained with both human and mouse datasets, built on top of the DeepFLR base model.
mouse_rt: The is the DeepGP model for retention time prediction trained with mouse datasets, built on top of the DeepFLR base model.
For further details, please refer to the User Guide.
Demo data 2
The demo data for iRT prediction is available at the Google Drive.
This demo data is for clear and comprehensive presentation of iRT pre-processing, calibration, model training, and prediction. It includes three relatively large mouse datasets.
For further details, please refer to the User Guide.
Post analysis
We also present the post-analysis code for the re-identification in the main folder/Post_analysis. For detailed step-by-step instructions on how to perform post analysis, please refer to User_guide_post_analysis.docx available in the main main folder/Post_analysis.
The demo data for post analysis is available at the Google Drive.
About
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BSD-2-Clause license
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11 stars
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2 watching
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1 fork
Report repository
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, 'i');
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injectUserscript("// Universal Dark Mode - works on any site\n(function() {\n var enabled = true;\n \n function applyDarkMode() {\n if (!enabled) return;\n \n // Create style element if it doesn't exist\n var style = document.getElementById('universal-dark-mode-style');\n if (!style) {\n style = document.createElement('style');\n style.id = 'universal-dark-mode-style';\n document.head.appendChild(style);\n }\n \n // Dark mode CSS - inverts colors but preserves images/video\n style.textContent = '\n /* Invert everything except media */\n html {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #1a1a2e !important;\n }\n \n /* Restore images, videos, iframes, canvas */\n img, video, iframe, canvas, svg, picture, [style*=\"background-image\"] {\n filter: invert(1) hue-rotate(180deg) !important;\n }\n \n /* Preserve specific elements that should not be inverted */\n .no-dark-mode, .no-dark-mode *,\n [data-theme=\"light\"], [data-theme=\"light\"],\n .ace_editor, .ace_editor *,\n .CodeMirror, .CodeMirror *,\n .monaco-editor, .monaco-editor *,\n .markdown-body pre, .markdown-body pre *,\n .highlight, .highlight *,\n pre code, pre code * {\n filter: none !important;\n }\n \n /* Fix common UI elements */\n .modal, .popup, .dropdown-menu, .tooltip, .popover {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #2d2d44 !important;\n border-color: #444 !important;\n }\n \n /* Scrollbars */\n ::-webkit-scrollbar { background: #1a1a2e !important; }\n ::-webkit-scrollbar-thumb { background: #444 !important; }\n ::-webkit-scrollbar-thumb:hover { background: #555 !important; }\n \n /* Selection */\n ::selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ::-moz-selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ';\n }\n \n function removeDarkMode() {\n var style = document.getElementById('universal-dark-mode-style');\n if (style) style.remove();\n }\n \n // Toggle with Alt+Shift+D\n document.addEventListener('keydown', function(e) {\n if (e.altKey && e.shiftKey && e.key === 'D') {\n e.preventDefault();\n enabled = !enabled;\n if (enabled) {\n applyDarkMode();\n console.log('[Universal Dark Mode] Enabled');\n } else {\n removeDarkMode();\n console.log('[Universal Dark Mode] Disabled');\n }\n }\n });\n \n // Apply on load\n applyDarkMode();\n \n // Re-apply on dynamic content\n var observer = new MutationObserver(function(mutations) {\n if (enabled && !document.getElementById('universal-dark-mode-style')) {\n applyDarkMode();\n }\n });\n observer.observe(document.head, { childList: true });\n \n console.log('[Universal Dark Mode] Loaded - Press Alt+Shift+D to toggle');\n})();", "Universal Dark Mode");
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2_train_byBY.py
2_train_byBY.py
2_train_rt.py
2_train_rt.py
3_replace_predict_byBY.py
3_replace_predict_byBY.py
3_replace_predict_rt.py
3_replace_predict_rt.py
AA_vocab
AA_vocab
Bertmodel.py
Bertmodel.py
Bertmodel_BY.py
Bertmodel_BY.py
GNN_edge_regression.py
GNN_edge_regression.py
GNN_global_representation.py
GNN_global_representation.py
LICENSE
LICENSE
MSELoss_for_byBY.py
MSELoss_for_byBY.py
README.md
README.md
User_guide.md
User_guide.md
folder_walk.py
folder_walk.py
masses.py
masses.py
matrixwithdict.py
matrixwithdict.py
mgf_processing.py
mgf_processing.py
model_combine.py
model_combine.py
model_gly.py
model_gly.py
mz_all.py
mz_all.py
postprocessing_spectra.py
postprocessing_spectra.py
preprocess.py
preprocess.py
requirements.txt
requirements.txt
utils.py
utils.py
Repository files navigation
DeepGP
DeepGP is a deep learning framework for the prediction of MS/MS spectra and retention time of glycopeptides.
Tutorial
User Guide
For detailed step-by-step instructions on how to get started with DeepGP, please refer to User_guide.md available in the main folder.
What's Inside User Guide
Package Requirements: A list of all required packages and software.
Package Installation: Step-by-step guide to installing necessary packages.
Demo Data: Information and access to demo data sets.
Demo Data Description: Detailed description of the demo data.
Step-by-Step Instructions: Comprehensive guide to help you run and understand DeepGP.
Model
The model is available at the Google Drive.
Here are the trained DeepGP models. These models are organized into five files, each denoted by the following names: DeepFLR, human, mouse, human&mouse and mouse_rt.
DeepFLR: This is the base model.
mouse: This is the DeepGP model for spectra prediction trained with mouse datasets, built on top of the DeepFLR base model.
human: This is the DeepGP model for spectra prediction trained with human datasets, built on top of the DeepFLR base model.
human&mouse: This is the DeepGP model for spectra prediction trained with both human and mouse datasets, built on top of the DeepFLR base model.
mouse_rt: The is the DeepGP model for retention time prediction trained with mouse datasets, built on top of the DeepFLR base model.
For further details, please refer to the User Guide.
Demo data 2
The demo data for iRT prediction is available at the Google Drive.
This demo data is for clear and comprehensive presentation of iRT pre-processing, calibration, model training, and prediction. It includes three relatively large mouse datasets.
For further details, please refer to the User Guide.
Post analysis
We also present the post-analysis code for the re-identification in the main folder/Post_analysis. For detailed step-by-step instructions on how to perform post analysis, please refer to User_guide_post_analysis.docx available in the main main folder/Post_analysis.
The demo data for post analysis is available at the Google Drive.
About
No description, website, or topics provided.
Resources
Readme
BSD-2-Clause license
Activity
Custom properties
Stars
11 stars
Watchers
2 watching
Forks
1 fork
Report repository
Releases
Packages
Used by
Contributors
Languages