College of Information Engineering, Shanghai Maritime University, Shanghai 201306, China.
School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China.
Biomolecules. 2024 Sep 2;14(9):1105. doi: 10.3390/biom14091105.
The classification of missense variant pathogenicity continues to pose significant challenges in human genetics, necessitating precise predictions of functional impacts for effective disease diagnosis and personalized treatment strategies. Traditional methods, often compromised by suboptimal feature selection and limited generalizability, are outpaced by the enhanced classification model, MissenseNet (Missense Classification Network). This model, advancing beyond standard predictive features, incorporates structural insights from AlphaFold2 protein predictions, thus optimizing structural data utilization. MissenseNet, built on the ShuffleNet architecture, incorporates an encoder-decoder framework and a Squeeze-and-Excitation (SE) module designed to adaptively adjust channel weights and enhance feature fusion and interaction. The model's efficacy in classifying pathogenicity has been validated through superior accuracy compared to conventional methods and by achieving the highest areas under the Receiver Operating Characteristic (ROC) and Precision-Recall (PR) curves (Area Under the Curve and Area Under the Precision-Recall Curve) in an independent test set, thus underscoring its superiority.
错义变异致病性的分类在人类遗传学中仍然具有重大挑战，需要进行精确的功能影响预测，以实现有效的疾病诊断和个性化治疗策略。传统方法往往受到特征选择不佳和通用性有限的限制，已经被先进的分类模型 MissenseNet（错义分类网络）所超越。该模型超越了标准的预测特征，纳入了 AlphaFold2 蛋白质预测的结构见解，从而优化了结构数据的利用。基于 ShuffleNet 架构构建的 MissenseNet 采用了编码器-解码器框架和 Squeeze-and-Excitation（SE）模块，旨在自适应地调整通道权重，增强特征融合和交互。通过与传统方法相比具有更高的准确性，以及在独立测试集中获得最高的接收者操作特征（ROC）和精度-召回率（PR）曲线下面积（曲线下面积和精度-召回率曲线下面积），该模型在致病性分类方面的效果得到了验证，这突显了其优越性。
