APDCA: An accurate and effective method for predicting associations between RBPs and AS-events during epithelial-mesenchymal transition

Motivation: Epithelial-mesenchymal transition (EMT) plays a key role in cancer metastasis by promoting changes in adhesion and motility. RNA-binding proteins (RBPs) regulate alternative splicing (AS) during EMT, enabling a single gene to produce multiple protein isoforms that affect tumor progressio...

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Vydané v:PLoS computational biology Ročník 21; číslo 11; s. e1013665
Hlavní autori: He, Yangsong, Bai, Zheng-Jian, Ching, Wai-Ki, Zou, Quan, Qiu, Yushan
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: United States Public Library of Science (PLoS) 01.11.2025
Predmet:
Algorithms
Alternative Splicing - genetics
Computational Biology - methods
Epithelial-Mesenchymal Transition - genetics
Humans
Neoplasms - genetics
Neoplasms - metabolism
RNA-Binding Proteins - genetics
RNA-Binding Proteins - metabolism
ISSN:1553-7358, 1553-734X, 1553-7358
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Shrnutí:Motivation: Epithelial-mesenchymal transition (EMT) plays a key role in cancer metastasis by promoting changes in adhesion and motility. RNA-binding proteins (RBPs) regulate alternative splicing (AS) during EMT, enabling a single gene to produce multiple protein isoforms that affect tumor progression. Disruption of RBP-AS interactions may disrupt the progress of diseases like cancer. Despite the importance of RBP-AS relationships in EMT, few computational methods predict these associations. Existing models struggle in sparse settings with limited known associations. To improve performance, we incorporate both sparsity constraints and heterogeneous biological data to infer RBP–AS associations. Result: We propose a new method based on A ccelerated P roximal DC A lgorithm (APDCA) for predicting RBP–AS associations. In particular, APDCA combines sparse low-rank matrix factorization with a Difference-of-Convex (DC) optimization framework and uses extrapolation to improve convergence. A key feature of APDCA is the use of a sparsity constraint, which filters out noise and highlights key associations. In addition, integrating multiple related data sources with direct or indirect relationships can help in reaching a more comprehensive view of RBPs and AS events and to reduce the impact of false positives associated with individual data sources. we prove that our proposed algorithm is convergent under some conditions and the experimental results have illustrated that APDCA outperforms six baseline methods in both AUC and AUPR. A case study on the RBP QKI shows that the top predictions are verified by the OncoSplicing database. Thus, APDCA provides a fast, interpretable, and scalable tool for discovering post-transcriptional regulatory interactions.
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ISSN:1553-7358
1553-734X
1553-7358
DOI:10.1371/journal.pcbi.1013665