GraphLncLoc: long non -coding RNA subcellular localization prediction using graph convolutional networks based on sequence to graph transformation
GraphLncLoc: long non -coding RNA subcellular localization prediction using graph convolutional networks based on sequence to graph transformation
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DOI：
10.1093/bib/bbac565
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发表时间：
2022-12-21
影响因子：
9.5
通讯作者：
Zeng, Min
中科院分区：
文献类型：
--
作者：
Li, Min;Zhao, Baoying;Zeng, Min
长链非编码RNA（lncRNA）的亚细胞定位对于理解lncRNA的功能至关重要.现有的1 ncRNA亚细胞定位预测方法大多使用k-mer频率特征来编码1 ncRNA序列。然而，k-mer频率特征丢失了序列顺序信息，并且不能捕获不同长度的序列模式和基序。在本文中，我们提出了GraphLncLoc，一种基于图卷积网络的深度学习模型，用于预测1 ncRNA亚细胞定位。与以往的研究不同，GraphLncLoc将1 ncRNA序列转化为de Bruijn图，将序列分类问题转化为图分类问题。为了从de Bruijn图中提取高级特征，GraphLncLoc采用图卷积网络来学习潜在表示。然后，从de Bruijn图导出的高级特征向量被馈送到全连接层以执行预测任务。大量的实验表明，GraphLncLoc比传统的机器学习模型和现有的预测器具有更好的性能。此外，我们的分析表明，将序列转化为图具有更多的可区分的功能，比k-mer频率功能更强大。案例研究表明，GraphLncLoc可以揭示重要的基序的核亚细胞定位。GraphLncLoc网络服务器可在http://csuligroup.com:8000/GraphLncLoct上获得。
The subcellular localization of long non -coding RNAs (lncRNAs) is crucial for understanding 1ncRNA functions. Most of existing 1ncRNA subcellular localization prediction methods use k-mer frequency features to encode 1ncRNA sequences. However, k-mer frequency features lose sequence order information and fail to capture sequence patterns and motifs of different lengths. In this paper, we proposed GraphLncLoc, a graph convolutional network -based deep learning model, for predicting 1ncRNA subcellular localization. Unlike previous studies encoding 1ncRNA sequences by using k-mer frequency features, GraphLncLoc transforms 1ncRNA sequences into de Bruijn graphs, which transforms the sequence classification problem into a graph classification problem. To extract the high-level features from the de Bruijn graph, GraphLncLoc employs graph convolutional networks to learn latent representations. Then, the high-level feature vectors derived from de Bruijn graph are fed into a fully connected layer to perform the prediction task. Extensive experiments show that GraphLncLoc achieves better performance than traditional machine learning models and existing predictors. In addition, our analyses show that transforming sequences into graphs has more distinguishable features and is more robust than k-mer frequency features. The case study shows that GraphLncLoc can uncover important motifs for nucleus subcellular localization. GraphLncLoc web server is available at http://csuligroup.com:8000/GraphLncLoct.