PCDA-HNMP: Predicting circRNA-disease association using heterogeneous network and meta-path
Increasing amounts of experimental studies have shown that circular RNAs (circRNAs) play important regulatory roles in human diseases through interactions with related microRNAs (miRNAs). CircRNAs have become new potential disease biomarkers and therapeutic targets. Predicting circRNA-disease associ...
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| Format: | Article |
| Language: | English |
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AIMS Press
2023-11-01
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| Series: | Mathematical Biosciences and Engineering |
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| Online Access: | https://www.aimspress.com/article/doi/10.3934/mbe.2023909?viewType=HTML |
| _version_ | 1797403744574373888 |
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| author | Lei Chen Xiaoyu Zhao |
| author_facet | Lei Chen Xiaoyu Zhao |
| author_sort | Lei Chen |
| collection | DOAJ |
| description | Increasing amounts of experimental studies have shown that circular RNAs (circRNAs) play important regulatory roles in human diseases through interactions with related microRNAs (miRNAs). CircRNAs have become new potential disease biomarkers and therapeutic targets. Predicting circRNA-disease association (CDA) is of great significance for exploring the pathogenesis of complex diseases, which can improve the diagnosis level of diseases and promote the targeted therapy of diseases. However, determination of CDAs through traditional clinical trials is usually time-consuming and expensive. Computational methods are now alternative ways to predict CDAs. In this study, a new computational method, named PCDA-HNMP, was designed. For obtaining informative features of circRNAs and diseases, a heterogeneous network was first constructed, which defined circRNAs, mRNAs, miRNAs and diseases as nodes and associations between them as edges. Then, a deep analysis was conducted on the heterogeneous network by extracting meta-paths connecting to circRNAs (diseases), thereby mining hidden associations between various circRNAs (diseases). These associations constituted the meta-path-induced networks for circRNAs and diseases. The features of circRNAs and diseases were derived from the aforementioned networks via mashup. On the other hand, miRNA-disease associations (mDAs) were employed to improve the model's performance. miRNA features were yielded from the meta-path-induced networks on miRNAs and circRNAs, which were constructed from the meta-paths connecting miRNAs and circRNAs in the heterogeneous network. A concatenation operation was adopted to build the features of CDAs and mDAs. Such representations of CDAs and mDAs were fed into XGBoost to set up the model. The five-fold cross-validation yielded an area under the curve (AUC) of 0.9846, which was better than those of some existing state-of-the-art methods. The employment of mDAs can really enhance the model's performance and the importance analysis on meta-path-induced networks shown that networks produced by the meta-paths containing validated CDAs provided the most important contributions. |
| first_indexed | 2024-03-09T02:42:51Z |
| format | Article |
| id | doaj.art-f73f2fcfffb7478dae11a639b4c884b4 |
| institution | Directory Open Access Journal |
| issn | 1551-0018 |
| language | English |
| last_indexed | 2024-03-09T02:42:51Z |
| publishDate | 2023-11-01 |
| publisher | AIMS Press |
| record_format | Article |
| series | Mathematical Biosciences and Engineering |
| spelling | doaj.art-f73f2fcfffb7478dae11a639b4c884b42023-12-06T01:30:54ZengAIMS PressMathematical Biosciences and Engineering1551-00182023-11-012012205532057510.3934/mbe.2023909PCDA-HNMP: Predicting circRNA-disease association using heterogeneous network and meta-pathLei Chen0Xiaoyu Zhao1College of Information Engineering, Shanghai Maritime University, Shanghai 201306, ChinaCollege of Information Engineering, Shanghai Maritime University, Shanghai 201306, ChinaIncreasing amounts of experimental studies have shown that circular RNAs (circRNAs) play important regulatory roles in human diseases through interactions with related microRNAs (miRNAs). CircRNAs have become new potential disease biomarkers and therapeutic targets. Predicting circRNA-disease association (CDA) is of great significance for exploring the pathogenesis of complex diseases, which can improve the diagnosis level of diseases and promote the targeted therapy of diseases. However, determination of CDAs through traditional clinical trials is usually time-consuming and expensive. Computational methods are now alternative ways to predict CDAs. In this study, a new computational method, named PCDA-HNMP, was designed. For obtaining informative features of circRNAs and diseases, a heterogeneous network was first constructed, which defined circRNAs, mRNAs, miRNAs and diseases as nodes and associations between them as edges. Then, a deep analysis was conducted on the heterogeneous network by extracting meta-paths connecting to circRNAs (diseases), thereby mining hidden associations between various circRNAs (diseases). These associations constituted the meta-path-induced networks for circRNAs and diseases. The features of circRNAs and diseases were derived from the aforementioned networks via mashup. On the other hand, miRNA-disease associations (mDAs) were employed to improve the model's performance. miRNA features were yielded from the meta-path-induced networks on miRNAs and circRNAs, which were constructed from the meta-paths connecting miRNAs and circRNAs in the heterogeneous network. A concatenation operation was adopted to build the features of CDAs and mDAs. Such representations of CDAs and mDAs were fed into XGBoost to set up the model. The five-fold cross-validation yielded an area under the curve (AUC) of 0.9846, which was better than those of some existing state-of-the-art methods. The employment of mDAs can really enhance the model's performance and the importance analysis on meta-path-induced networks shown that networks produced by the meta-paths containing validated CDAs provided the most important contributions.https://www.aimspress.com/article/doi/10.3934/mbe.2023909?viewType=HTMLcircrnadiseasecircrna-disease associationheterogeneous networkmeta-pathnetwork embedding algorithmmirna-disease association |
| spellingShingle | Lei Chen Xiaoyu Zhao PCDA-HNMP: Predicting circRNA-disease association using heterogeneous network and meta-path Mathematical Biosciences and Engineering circrna disease circrna-disease association heterogeneous network meta-path network embedding algorithm mirna-disease association |
| title | PCDA-HNMP: Predicting circRNA-disease association using heterogeneous network and meta-path |
| title_full | PCDA-HNMP: Predicting circRNA-disease association using heterogeneous network and meta-path |
| title_fullStr | PCDA-HNMP: Predicting circRNA-disease association using heterogeneous network and meta-path |
| title_full_unstemmed | PCDA-HNMP: Predicting circRNA-disease association using heterogeneous network and meta-path |
| title_short | PCDA-HNMP: Predicting circRNA-disease association using heterogeneous network and meta-path |
| title_sort | pcda hnmp predicting circrna disease association using heterogeneous network and meta path |
| topic | circrna disease circrna-disease association heterogeneous network meta-path network embedding algorithm mirna-disease association |
| url | https://www.aimspress.com/article/doi/10.3934/mbe.2023909?viewType=HTML |
| work_keys_str_mv | AT leichen pcdahnmppredictingcircrnadiseaseassociationusingheterogeneousnetworkandmetapath AT xiaoyuzhao pcdahnmppredictingcircrnadiseaseassociationusingheterogeneousnetworkandmetapath |