LPIH2V: LncRNA-protein interactions prediction using HIN2Vec based on heterogeneous networks model
LncRNA-protein interaction plays an important role in the development and treatment of many human diseases. As the experimental approaches to determine lncRNA–protein interactions are expensive and time-consuming, considering that there are few calculation methods, therefore, it is urgent to develop...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2023-02-01
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| Series: | Frontiers in Genetics |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fgene.2023.1122909/full |
| _version_ | 1811167219343687680 |
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| author | Meng-Meng Wei Chang-Qing Yu Li-Ping Li Li-Ping Li Zhu-Hong You Zhong-Hao Ren Yong-Jian Guan Xin-Fei Wang Yue-Chao Li |
| author_facet | Meng-Meng Wei Chang-Qing Yu Li-Ping Li Li-Ping Li Zhu-Hong You Zhong-Hao Ren Yong-Jian Guan Xin-Fei Wang Yue-Chao Li |
| author_sort | Meng-Meng Wei |
| collection | DOAJ |
| description | LncRNA-protein interaction plays an important role in the development and treatment of many human diseases. As the experimental approaches to determine lncRNA–protein interactions are expensive and time-consuming, considering that there are few calculation methods, therefore, it is urgent to develop efficient and accurate methods to predict lncRNA-protein interactions. In this work, a model for heterogeneous network embedding based on meta-path, namely LPIH2V, is proposed. The heterogeneous network is composed of lncRNA similarity networks, protein similarity networks, and known lncRNA-protein interaction networks. The behavioral features are extracted in a heterogeneous network using the HIN2Vec method of network embedding. The results showed that LPIH2V obtains an AUC of 0.97 and ACC of 0.95 in the 5-fold cross-validation test. The model successfully showed superiority and good generalization ability. Compared to other models, LPIH2V not only extracts attribute characteristics by similarity, but also acquires behavior properties by meta-path wandering in heterogeneous networks. LPIH2V would be beneficial in forecasting interactions between lncRNA and protein. |
| first_indexed | 2024-04-10T16:05:34Z |
| format | Article |
| id | doaj.art-183f590cb67f456688bc137339ea8c6a |
| institution | Directory Open Access Journal |
| issn | 1664-8021 |
| language | English |
| last_indexed | 2024-04-10T16:05:34Z |
| publishDate | 2023-02-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Genetics |
| spelling | doaj.art-183f590cb67f456688bc137339ea8c6a2023-02-10T04:35:47ZengFrontiers Media S.A.Frontiers in Genetics1664-80212023-02-011410.3389/fgene.2023.11229091122909LPIH2V: LncRNA-protein interactions prediction using HIN2Vec based on heterogeneous networks modelMeng-Meng Wei0Chang-Qing Yu1Li-Ping Li2Li-Ping Li3Zhu-Hong You4Zhong-Hao Ren5Yong-Jian Guan6Xin-Fei Wang7Yue-Chao Li8School of Information Engineering, Xijing University, Xi’an, ChinaSchool of Information Engineering, Xijing University, Xi’an, ChinaSchool of Information Engineering, Xijing University, Xi’an, ChinaCollege of Grassland and Environment Sciences, Xinjiang Agricultural University, Urumqi, ChinaSchool of Computer Science, Northwestern Polytechnical University, Xi’an, ChinaSchool of Information Engineering, Xijing University, Xi’an, ChinaSchool of Information Engineering, Xijing University, Xi’an, ChinaXijing University, Xi’an, ChinaXijing University, Xi’an, ChinaLncRNA-protein interaction plays an important role in the development and treatment of many human diseases. As the experimental approaches to determine lncRNA–protein interactions are expensive and time-consuming, considering that there are few calculation methods, therefore, it is urgent to develop efficient and accurate methods to predict lncRNA-protein interactions. In this work, a model for heterogeneous network embedding based on meta-path, namely LPIH2V, is proposed. The heterogeneous network is composed of lncRNA similarity networks, protein similarity networks, and known lncRNA-protein interaction networks. The behavioral features are extracted in a heterogeneous network using the HIN2Vec method of network embedding. The results showed that LPIH2V obtains an AUC of 0.97 and ACC of 0.95 in the 5-fold cross-validation test. The model successfully showed superiority and good generalization ability. Compared to other models, LPIH2V not only extracts attribute characteristics by similarity, but also acquires behavior properties by meta-path wandering in heterogeneous networks. LPIH2V would be beneficial in forecasting interactions between lncRNA and protein.https://www.frontiersin.org/articles/10.3389/fgene.2023.1122909/fulllncRNA-protein interactionheterogeneous information networknetwork embeddingHIN2Vecbehavioral features |
| spellingShingle | Meng-Meng Wei Chang-Qing Yu Li-Ping Li Li-Ping Li Zhu-Hong You Zhong-Hao Ren Yong-Jian Guan Xin-Fei Wang Yue-Chao Li LPIH2V: LncRNA-protein interactions prediction using HIN2Vec based on heterogeneous networks model Frontiers in Genetics lncRNA-protein interaction heterogeneous information network network embedding HIN2Vec behavioral features |
| title | LPIH2V: LncRNA-protein interactions prediction using HIN2Vec based on heterogeneous networks model |
| title_full | LPIH2V: LncRNA-protein interactions prediction using HIN2Vec based on heterogeneous networks model |
| title_fullStr | LPIH2V: LncRNA-protein interactions prediction using HIN2Vec based on heterogeneous networks model |
| title_full_unstemmed | LPIH2V: LncRNA-protein interactions prediction using HIN2Vec based on heterogeneous networks model |
| title_short | LPIH2V: LncRNA-protein interactions prediction using HIN2Vec based on heterogeneous networks model |
| title_sort | lpih2v lncrna protein interactions prediction using hin2vec based on heterogeneous networks model |
| topic | lncRNA-protein interaction heterogeneous information network network embedding HIN2Vec behavioral features |
| url | https://www.frontiersin.org/articles/10.3389/fgene.2023.1122909/full |
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