标题： 将生物知识与因子图神经网络结合以实现可解释的深度学习 显示英文标题

标题： Incorporating Biological Knowledge with Factor Graph Neural Network for Interpretable Deep Learning

摘要： 虽然深度学习在许多领域取得了巨大成功，但关于深度学习的一个常见批评是其缺乏可解释性。 在大多数情况下，深度神经网络中的隐藏单元没有明确的语义意义或对应任何物理实体。 然而，在许多生物医学应用中，模型的可解释性和可说明性至关重要。 为了解决这一挑战，我们开发了因子图神经网络模型，通过将概率图模型与深度学习相结合，使其具有可解释性和可预测性。 我们将生物学知识，如基因本体论，直接编码为因子图到模型架构中，使模型透明且可解释。 此外，我们设计了一种注意力机制，可以捕捉生物实体（如基因和基因本体术语）之间的多尺度分层交互作用。 通过参数共享机制，展开的因子图神经网络模型可以使用随机深度进行训练并具有良好泛化能力。 我们将该模型应用于两个癌症基因组数据集，以预测目标临床变量，并取得了比其他传统机器学习和深度学习模型更好的结果。 我们的模型还可以用于基因集富集分析，并选择对目标临床变量重要的基因本体术语。 显示英文摘要

摘要： While deep learning has achieved great success in many fields, one common criticism about deep learning is its lack of interpretability. In most cases, the hidden units in a deep neural network do not have a clear semantic meaning or correspond to any physical entities. However, model interpretability and explainability are crucial in many biomedical applications. To address this challenge, we developed the Factor Graph Neural Network model that is interpretable and predictable by combining probabilistic graphical models with deep learning. We directly encode biological knowledge such as Gene Ontology as a factor graph into the model architecture, making the model transparent and interpretable. Furthermore, we devised an attention mechanism that can capture multi-scale hierarchical interactions among biological entities such as genes and Gene Ontology terms. With parameter sharing mechanism, the unrolled Factor Graph Neural Network model can be trained with stochastic depth and generalize well. We applied our model to two cancer genomic datasets to predict target clinical variables and achieved better results than other traditional machine learning and deep learning models. Our model can also be used for gene set enrichment analysis and selecting Gene Ontology terms that are important to target clinical variables.