Visualizing and Annotating Protein Sequences using A Deep Neural Network
Visualizing and Annotating Protein Sequences using A Deep Neural Network
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DOI：
10.1109/ieeeconf51394.2020.9443364
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发表时间：
2020-11
期刊：
影响因子：
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通讯作者：
Zhengqiao Zhao;G. Rosen
中科院分区：
文献类型：
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作者：
Zhengqiao Zhao;G. Rosen
对氨基酸序列进行注释和了解蛋白质的功能对于生物学研究至关重要。蛋白质功能对健康产业中的医学研究很重要（例如，药物发现）。随着深度学习技术的发展，人们已经开发出了精确的蛋白质注释模型来进行无比对的蛋白质注释。在本文中，我们开发了一个具有注意力机制的深度学习模型，该模型可以在给定蛋白质序列输入的情况下预测基因本体标签。我们相信这个模型可以产生准确的预测，并保持良好的可解释性。我们进一步展示了如何通过检查和可视化深度神经网络中的中间层输出来解释模型。
It is critical for biological studies to annotate amino acid sequences and understand how proteins function. Protein function is important to medical research in the health industry (e.g., drug discovery). With the advancement of deep learning, accurate protein annotation models have been developed for alignment free protein annotation. In this paper, we develop a deep learning model with an attention mechanism that can predict Gene Ontology labels given a protein sequence input. We believe this model can produce accurate predictions as well as maintain good interpretability. We further show how the model can be interpreted by examining and visualizing the intermediate layer output in our deep neural network.