AGML: Adaptive Graph-based Multi-label Learning for Prediction of RBP and AS Event Associations During EMT.

IF 3.6 3区 生物学 Q2 BIOCHEMICAL RESEARCH METHODS IEEE/ACM Transactions on Computational Biology and Bioinformatics Pub Date : 2024-08-12 DOI:10.1109/TCBB.2024.3440913
Yushan Qiu, Wensheng Chen, Wai-Ki Ching, Hongmin Cai, Hao Jiang, Quan Zou
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Abstract

Increasing evidence has indicated that RNA-binding proteins (RBPs) play an essential role in mediating alternative splicing (AS) events during epithelial-mesenchymal transition (EMT). However, due to the substantial cost and complexity of biological experiments, how AS events are regulated and influenced remains largely unknown. Thus, it is important to construct effective models for inferring hidden RBP-AS event associations during EMT process. In this paper, a novel and efficient model was developed to identify AS event-related candidate RBPs based on Adaptive Graph-based Multi-Label learning (AGML). In particular, we propose to adaptively learn a new affinity graph to capture the intrinsic structure of data for both RBPs and AS events. Multi-view similarity matrices are employed for maintaining the intrinsic structure and guiding the adaptive graph learning. We then simultaneously update the RBP and AS event associations that are predicted from both spaces by applying multi-label learning. The experimental results have shown that our AGML achieved AUC values of 0.9521 and 0.9873 by 5-fold and leave-one-out cross-validations, respectively, indicating the superiority and effectiveness of our proposed model. Furthermore, AGML can serve as an efficient and reliable tool for uncovering novel AS events-associated RBPs and is applicable for predicting the associations between other biological entities. The source code of AGML is available at https://github.com/yushanqiu/AGML.

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AGML:基于图形的自适应多标签学习,用于预测 EMT 期间的 RBP 和 AS 事件关联。
越来越多的证据表明,在上皮-间质转化(EMT)过程中,RNA 结合蛋白(RBPs)在介导替代剪接(AS)事件中起着至关重要的作用。然而,由于生物实验成本高昂且十分复杂,AS 事件如何受到调控和影响在很大程度上仍是未知数。因此,构建有效的模型来推断 EMT 过程中隐藏的 RBP-AS 事件关联非常重要。本文基于基于自适应图的多标签学习(AGML),开发了一种新颖高效的模型来识别AS事件相关的候选RBP。特别是,我们建议自适应学习一种新的亲和图,以捕捉 RBPs 和 AS 事件的数据内在结构。多视图相似性矩阵用于保持内在结构和指导自适应图学习。然后,我们通过应用多标签学习,同时更新从两个空间预测出的 RBP 和 AS 事件关联。实验结果表明,通过五倍交叉验证和留一交叉验证,我们的 AGML 的 AUC 值分别达到了 0.9521 和 0.9873,这表明我们提出的模型是优越和有效的。此外,AGML可以作为发现新型AS事件相关RBPs的高效可靠工具,并适用于预测其他生物实体之间的关联。AGML 的源代码见 https://github.com/yushanqiu/AGML。
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来源期刊
CiteScore
7.50
自引率
6.70%
发文量
479
审稿时长
3 months
期刊介绍: IEEE/ACM Transactions on Computational Biology and Bioinformatics emphasizes the algorithmic, mathematical, statistical and computational methods that are central in bioinformatics and computational biology; the development and testing of effective computer programs in bioinformatics; the development of biological databases; and important biological results that are obtained from the use of these methods, programs and databases; the emerging field of Systems Biology, where many forms of data are used to create a computer-based model of a complex biological system
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