This repository contains the Allergen Chip data challenge result including best notebooks source code and documentation.
The Allergen Chip Challenge, a collaborative project led by the French Society of Allergology (SFA) in partnership with the Health Data Hub, focuses on personalized medicine through exploring the antibody profile (immunoglobulin E) developed by each patient in response to their environment.
The goal of the competition was to develop Machine Learning models that can predict the presence and severity of an allergic disease based on this personalized profile.
- Understand the relationship between exposome, IgE, and clinical expression of allergy, in particular :
- Efficiently diagnose and stratify allergic patients
- Gain a better understanding of IgE clusters in relation to age, symptoms, and climate area
- Tackle the complexity of immune response using AI methods
- 292 participants from multiple countries around the world
- 3135 submissions during the 3 months of the challenge
- Best F1 Macro score 0.786 on the private portion of the test set
All the models of the Allergen Chip challenge were built on trustii.io hosted Jupyterhub environnement, each team had access to a notebook session holding 4 vCPU and 16GB RAM and 20GB persistent storage.
| Ranking | Team | Score Public Leaderboard | Score Private Leaderboard | Winning model summary |
|---|---|---|---|---|
| 1 | newbee | 0.7729 | 0.7861 | Individual binary classifiers were developed for the 27 targets using LightGBM and catboost, with AUC as the training metric. An automated pipeline was created for feature selection and model training with cross-validation. For final predictions, a threshold was chosen for optimal F1 scores, and results from LightGBM and catboost were ensembled. Predictions were then adjusted based on learned target associations. |
| 2 | Rakesh Jarupula | 0.7708 | 0.7748 | The model's effectiveness is largely attributed to feature engineering. Key steps include data pre-processing, where rows and columns were dropped or modified to align train-test distributions, and an allergen-to-treatment mapping was developed. Features were engineered based on row and column statistics, while irrelevant allergen proteins were dropped. Model training was target-specific, using features relevant to each allergen. To address class imbalances, the scale_pos_weight parameter was used. Bayesian Search was employed for hyperparameter tuning with Repeated Stratified KFold for training. Predictions from three models were averaged to avoid overfitting. |
| 3 | Mithil | 0.7670 | 0.7719 | For preprocessing, Multilabel Stratified K-Fold cross-validation was used along with feature engineering, including row-wise statistics and interaction features, which boosted the model's F1 score. The most effective model was a finely-tuned XGBoost, wrapped in a MultiOutputClassifier, after testing various architectures. During post-processing, rather than a standard 0.5 threshold, individual optimal thresholds were determined for each target, improving the model's F1 performance. A noticeable correlation was found between positive samples in a target and its threshold. This tailored threshold approach played a pivotal role in enhancing the model's accuracy |
For more details check out each winning solution report and source code in the 'repository' above.
The dataset has been provided by Société Française of Allergology (SFA) and prepared with Trustii.io data scientists. The dataset has been constructed from data of more than 4,000 patients includes tabular data associated with image files.
If you are interested by accessing the dataset or collaborating with Trustii.io and SFA on this project, please reach out to us at contact@trustii.io.
To access the challenge forum discussions and the dataset description, check out the challenge webpage at https://app.trustii.io.