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https://github.com/mariomarroquim/eplogic
Scripts for Anti-HLA antibody target prediction via machine learning
https://github.com/mariomarroquim/eplogic
cross-validation hla-eplets hla-matching imbalanced-data immunoinformatics immunology machine-learning scikit-learn single-antigen-beads
Last synced: about 14 hours ago
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Scripts for Anti-HLA antibody target prediction via machine learning
- Host: GitHub
- URL: https://github.com/mariomarroquim/eplogic
- Owner: mariomarroquim
- License: mit
- Created: 2020-01-27T20:49:29.000Z (about 5 years ago)
- Default Branch: master
- Last Pushed: 2021-06-02T19:27:29.000Z (over 3 years ago)
- Last Synced: 2023-03-22T14:23:56.155Z (almost 2 years ago)
- Topics: cross-validation, hla-eplets, hla-matching, imbalanced-data, immunoinformatics, immunology, machine-learning, scikit-learn, single-antigen-beads
- Language: HTML
- Homepage:
- Size: 2.16 MB
- Stars: 3
- Watchers: 1
- Forks: 1
- Open Issues: 1
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Metadata Files:
- Readme: README.md
- License: LICENSE.txt
Awesome Lists containing this project
README
EpLogic
=======
Scripts for Anti-HLA antibody target prediction via machine learning.
Description
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These datasets comprise mismatched eplets from HLA alleles tested in single antigen panels of patients waiting for a solid organ transplantation. The experiments described here have the aim of classifying each eplet/panel/patient as reactive or non-reactive. Tip: reactive eplets usually have greater MFI values.
Experiments
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* Simple train/test validation. 89% accuracy and 88% AUC-ROC.
* Cross-validation. 88% accuracy and 92% AUC-ROC.
* Cross-validation & feature selection. 89% accuracy and 94% AUC-ROC.
* Cross-validation & feature selection & hyperparameter tuning. 91% accuracy and 94% AUC-ROC.
Support
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You can contact me at [email protected].