用AI模型NEO加速癌症疫苗靶点筛选，提升预测准确率。
Revolutionizing Personalized Cancer Vaccines with NEO: Novel Epitope Optimization Using an Aggregated Feed Forward and Recurrent Neural Network with LSTM Architecture
- 融合前馈与循环神经网络，同时处理序列和非序列数据。
- 在测试中达AUC 0.9166，召回率达91.67%。
- 适合肿瘤精准治疗研究者快速筛选有效疫苗靶点。
随着癌症病例持续上升，2023年浙江大学与哈佛大学的研究预测到2030年病例将增长31%，死亡人数增加21%。传统化疗因缺乏靶向性会伤害健康细胞，而个性化癌症疫苗可通过识别癌细胞上的独特新抗原（neoepitopes）实现精准治疗。但新抗原的筛选耗时且昂贵，依赖现代预测方法。本研究提出NEO模型，利用下一代测序数据，通过堆叠集成方法结合MHCFlurry 1.6、NetMHCstabpan 1.0和IEDB等先进模型得分。其架构包含前馈神经网络（FFNN）与带有LSTM层的循环神经网络（RNN），可同时分析序列与非序列特征，最终聚合输出预测结果。实验表明，该模型在新抗原结合预测上表现优异，达到AUC 0.9166，召回率91.67%，显著提升个性化癌症疫苗设计效率与准确性。
原文摘要 · Abstract (English)
As cancer cases continue to rise, with a 2023 study from Zhejiang and Harvard predicting a 31 percent increase in cases and a 21 percent increase in deaths by 2030, the need to find more effective treatments for cancer is greater than ever before. Traditional approaches to treating cancer, such as chemotherapy, often kill healthy cells because of their lack of targetability. In contrast, personalized cancer vaccines can utilize neoepitopes - distinctive peptides on cancer cells that are often missed by the body's immune system - that have strong binding affinities to a patient's MHC to provide a more targeted treatment approach. The selection of optimal neoepitopes that elicit an immune response is a time-consuming and costly process due to the required inputs of modern predictive methods. This project aims to facilitate faster, cheaper, and more accurate neoepitope binding predictions using Feed Forward Neural Networks (FFNN) and Recurrent Neural Networks (RNN). To address this, NEO was created. NEO requires next-generation sequencing data and uses a stacking ensemble method by calculating scores from state-of-the-art models (MHCFlurry 1.6, NetMHCstabpan 1.0, and IEDB). The model's architecture includes an FFNN and an RNN with LSTM layers capable of analyzing both sequential and non-sequential data. The results from both models are aggregated to produce predictions. Using this model, personalized cancer vaccines can be produced with improved results (AUC = 0.9166, recall = 91.67 percent).
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