This study presents an ensemble model combining LSTM, BiLSTM, CNN, GRU, and GloVe to classify gene mutations using Kaggle's Personalized Medicine: Redefining Cancer Treatment dataset. The results were compared against well-known transformers like as BERT, Electra, Roberta, XLNet, Distilbert, and their LSTM ensembles. Our model outperformed all other models in terms of accuracy, precision, recall, F1 score, and Mean Squared Error. Surprisingly, it also needed less training time, resulting in a perfect combination of performance and efficiency. This study demonstrates the utility of ensemble models for difficult tasks such as gene mutation classification.

翻译：本研究提出了一种集成模型，结合了LSTM、BiLSTM、CNN、GRU和GloVe，利用Kaggle的个性化医疗：重新定义癌症治疗数据集进行基因突变分类。将结果与著名的Transformer模型（如BERT、Electra、RoBERTa、XLNet、DistilBERT及其LSTM集成模型）进行了比较。我们的模型在准确率、精确率、召回率、F1分数和均方误差方面均优于所有其他模型。令人惊讶的是，该模型所需的训练时间也更少，实现了性能与效率的完美结合。本研究证明了集成模型在基因突变分类等复杂任务中的实用性。