Predicting circRNA-drug sensitivity associations via graph attention auto-encoder
Abstract Background Circular RNAs (circRNAs) play essential roles in cancer development and therapy resistance. Many studies have shown that circRNA is closely related to human health. The expression of circRNAs also affects the sensitivity of cells to drugs, thereby significantly affecting the effi...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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BMC
2022-05-01
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| Series: | BMC Bioinformatics |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12859-022-04694-y |
| _version_ | 1828785411082682368 |
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| author | Lei Deng Zixuan Liu Yurong Qian Jingpu Zhang |
| author_facet | Lei Deng Zixuan Liu Yurong Qian Jingpu Zhang |
| author_sort | Lei Deng |
| collection | DOAJ |
| description | Abstract Background Circular RNAs (circRNAs) play essential roles in cancer development and therapy resistance. Many studies have shown that circRNA is closely related to human health. The expression of circRNAs also affects the sensitivity of cells to drugs, thereby significantly affecting the efficacy of drugs. However, traditional biological experiments are time-consuming and expensive to validate drug-related circRNAs. Therefore, it is an important and urgent task to develop an effective computational method for predicting unknown circRNA-drug associations. Results In this work, we propose a computational framework (GATECDA) based on graph attention auto-encoder to predict circRNA-drug sensitivity associations. In GATECDA, we leverage multiple databases, containing the sequences of host genes of circRNAs, the structure of drugs, and circRNA-drug sensitivity associations. Based on the data, GATECDA employs Graph attention auto-encoder (GATE) to extract the low-dimensional representation of circRNA/drug, effectively retaining critical information in sparse high-dimensional features and realizing the effective fusion of nodes’ neighborhood information. Experimental results indicate that GATECDA achieves an average AUC of 89.18% under 10-fold cross-validation. Case studies further show the excellent performance of GATECDA. Conclusions Many experimental results and case studies show that our proposed GATECDA method can effectively predict the circRNA-drug sensitivity associations. |
| first_indexed | 2024-12-11T23:51:48Z |
| format | Article |
| id | doaj.art-0c606eaf7a5544ab8d0934c4b4dcaf88 |
| institution | Directory Open Access Journal |
| issn | 1471-2105 |
| language | English |
| last_indexed | 2024-12-11T23:51:48Z |
| publishDate | 2022-05-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Bioinformatics |
| spelling | doaj.art-0c606eaf7a5544ab8d0934c4b4dcaf882022-12-22T00:45:28ZengBMCBMC Bioinformatics1471-21052022-05-0123111510.1186/s12859-022-04694-yPredicting circRNA-drug sensitivity associations via graph attention auto-encoderLei Deng0Zixuan Liu1Yurong Qian2Jingpu Zhang3School of Software, Xinjiang UniversitySchool of Software, Xinjiang UniversitySchool of Software, Xinjiang UniversitySchool of Computer and Data Science, Henan University of Urban ConstructionAbstract Background Circular RNAs (circRNAs) play essential roles in cancer development and therapy resistance. Many studies have shown that circRNA is closely related to human health. The expression of circRNAs also affects the sensitivity of cells to drugs, thereby significantly affecting the efficacy of drugs. However, traditional biological experiments are time-consuming and expensive to validate drug-related circRNAs. Therefore, it is an important and urgent task to develop an effective computational method for predicting unknown circRNA-drug associations. Results In this work, we propose a computational framework (GATECDA) based on graph attention auto-encoder to predict circRNA-drug sensitivity associations. In GATECDA, we leverage multiple databases, containing the sequences of host genes of circRNAs, the structure of drugs, and circRNA-drug sensitivity associations. Based on the data, GATECDA employs Graph attention auto-encoder (GATE) to extract the low-dimensional representation of circRNA/drug, effectively retaining critical information in sparse high-dimensional features and realizing the effective fusion of nodes’ neighborhood information. Experimental results indicate that GATECDA achieves an average AUC of 89.18% under 10-fold cross-validation. Case studies further show the excellent performance of GATECDA. Conclusions Many experimental results and case studies show that our proposed GATECDA method can effectively predict the circRNA-drug sensitivity associations.https://doi.org/10.1186/s12859-022-04694-ycircRNA-drug associationsGraph attention auto-encoderNeural networkSimilarity network |
| spellingShingle | Lei Deng Zixuan Liu Yurong Qian Jingpu Zhang Predicting circRNA-drug sensitivity associations via graph attention auto-encoder BMC Bioinformatics circRNA-drug associations Graph attention auto-encoder Neural network Similarity network |
| title | Predicting circRNA-drug sensitivity associations via graph attention auto-encoder |
| title_full | Predicting circRNA-drug sensitivity associations via graph attention auto-encoder |
| title_fullStr | Predicting circRNA-drug sensitivity associations via graph attention auto-encoder |
| title_full_unstemmed | Predicting circRNA-drug sensitivity associations via graph attention auto-encoder |
| title_short | Predicting circRNA-drug sensitivity associations via graph attention auto-encoder |
| title_sort | predicting circrna drug sensitivity associations via graph attention auto encoder |
| topic | circRNA-drug associations Graph attention auto-encoder Neural network Similarity network |
| url | https://doi.org/10.1186/s12859-022-04694-y |
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