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SGCL-LncLoc:An Interpretable Deep Learning Model for Improving IncRNA subcellular localization prediction with Supervised Graph Contrastive Learning

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Big Data Mining and Analytics 2024年第3期7卷 765-780页

作者： Min Li Baoying Zhao Yiming Li Pingjian Ding Rui Yin Shichao Kan Min Zeng School of Computer Science and Engineering Central South UniversityChangsha 410083China Center for Artificial Intelligence in Drug Discovery Case Western Reserve UniversityClevelandOH 44106USA Department of Health Outcomes and Biomedical Informatics University of FloridaGainesvilleFL 32603USA

Understanding the subcellular localization of long non-coding RNAs(IncRNAs)is crucial for unraveling their functional *** previous computational methods have made progress in predicting IncRNA subcellular localization,most of them ignore the sequence order information by relying on k-mer frequency features to encode IncRNA *** the study,we develope SGCL-LncLoc,a novel interpretable deep learning model based on supervised graph contrastive ***-LncLoc transforms IncRNA sequences into de Bruijn graphs and uses the Word2Vec technique to learn the node representation of the ***,SGCL-LncLoc applies graph convolutional networks to learn the comprehensive graph ***,we propose a computational method to map the attention weights of the graph nodes to the weights of nucleotides in the IncRNA sequence,allowing SGCL-LncLoc to serve as an interpretable deep learning ***,SGCL-LncLoc employs a supervised contrastive learning strategy,which leverages the relationships between different samples and label information,guiding the model to enhance representation learning for *** experimental results demonstrate that SGCL-LncLoc outperforms both deep learning baseline models and existing predictors,showing its capability for accurate IncRNA subcellular localization ***,we conduct a motif analysis,revealing that SGCL-LncLoc successfully captures known motifs associated with IncRNA subcellular *** SGCL-LncLoc web server is available at http://***:8000/*** source code can be obtained from https://***/CSUBioGroup/SGCL-LncLoc.

关键词： supervised contrastive learning long non-coding RNA(IncRNA) subcellular localization prediction deep learning Graph Convolutional Network(GCN)

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