A Comparative Analysis of Novel Deep Learning and Ensemble Learning Models to Predict the Allergenicity of Food Proteins.
A Comparative Analysis of Novel Deep Learning and Ensemble Learning Models to Predict the Allergenicity of Food Proteins.
复制标题
预测食物蛋白质过敏性的新型深度学习和集成学习模型的比较分析
DOI：
10.3390/foods10040809
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发表时间：
2021-04-09
期刊：
影响因子：
--
通讯作者：
Che H
中科院分区：
文献类型：
--
作者：
Wang L;Niu D;Zhao X;Wang X;Hao M;Che H
传统的食品过敏原鉴定主要依靠体内和体外实验，往往周期长、成本高。人工智能（AI）驱动的快速食品过敏原识别方法解决了上述两个缺点，正在成为一种高效的辅助工具。针对传统机器学习模型在预测食物蛋白过敏性时准确率较低的局限性，提出引入深度学习模型--具有自注意机制的Transformer、集成学习模型（代表性为Light Gradient Boosting Machine（LightGBM）eXtreme Gradient Boosting（XGBoost））来解决这一问题。为了突出所提出的新方法的优越性，该研究还选择了各种常用的机器学习模型作为基线分类器。5倍交叉验证结果显示，深度模型的AUC最高（0.9578），优于集成学习和基线算法。但深度模型需要预先训练，训练成本最高。通过比较Transformer模型和Boosting模型的特点，可以看出，两种模型各有优势，为今后食物过敏原的快速预测提供了新的思路和启示。
Traditional food allergen identification mainly relies on in vivo and in vitro experiments, which often needs a long period and high cost. The artificial intelligence (AI)-driven rapid food allergen identification method has solved the above mentioned two drawbacks and is becoming an efficient auxiliary tool. Aiming to overcome the limitations of lower accuracy of traditional machine learning models in predicting the allergenicity of food proteins, this work proposed to introduce deep learning model - transformer with self-attention mechanism, ensemble learning models (representative as Light Gradient Boosting Machine (LightGBM) eXtreme Gradient Boosting (XGBoost)) to solve the problem. In order to highlight the superiority of the proposed novel method, the study also selected various commonly used machine learning models as the baseline classifiers. The results of 5-fold cross-validation showed that the AUC of the deep model was the highest (0.9578), which was better than the ensemble learning and baseline algorithms. But the deep model need to be pre-trained, and the training cost is the highest. By comparing the characteristics of the transformer model and boosting models, it can be analyzed that, each model has its own advantage, which provides novel clues and inspiration for the rapid prediction of food allergens in the future.
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DOI：
10.1109/tpami.2021.3095381
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