SGCNCMI: A New Model Combining Multi-Modal Information to Predict circRNA-Related miRNAs, Diseases and Genes
Computational prediction of miRNAs, diseases, and genes associated with circRNAs has important implications for circRNA research, as well as provides a reference for wet experiments to save costs and time. In this study, SGCNCMI, a computational model combining multimodal information and graph convo...
| Main Authors: | , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-09-01
|
| Series: | Biology |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2079-7737/11/9/1350 |
| _version_ | 1797491024113696768 |
|---|---|
| author | Chang-Qing Yu Xin-Fei Wang Li-Ping Li Zhu-Hong You Wen-Zhun Huang Yue-Chao Li Zhong-Hao Ren Yong-Jian Guan |
| author_facet | Chang-Qing Yu Xin-Fei Wang Li-Ping Li Zhu-Hong You Wen-Zhun Huang Yue-Chao Li Zhong-Hao Ren Yong-Jian Guan |
| author_sort | Chang-Qing Yu |
| collection | DOAJ |
| description | Computational prediction of miRNAs, diseases, and genes associated with circRNAs has important implications for circRNA research, as well as provides a reference for wet experiments to save costs and time. In this study, SGCNCMI, a computational model combining multimodal information and graph convolutional neural networks, combines node similarity to form node information and then predicts associated nodes using GCN with a distributive contribution mechanism. The model can be used not only to predict the molecular level of circRNA–miRNA interactions but also to predict circRNA–cancer and circRNA–gene associations. The AUCs of circRNA—miRNA, circRNA–disease, and circRNA–gene associations in the five-fold cross-validation experiment of SGCNCMI is 89.42%, 84.18%, and 82.44%, respectively. SGCNCMI is one of the few models in this field and achieved the best results. In addition, in our case study, six of the top ten relationship pairs with the highest prediction scores were verified in PubMed. |
| first_indexed | 2024-03-10T00:40:34Z |
| format | Article |
| id | doaj.art-ad1b1399078c469e927a4b1e2d0b86ab |
| institution | Directory Open Access Journal |
| issn | 2079-7737 |
| language | English |
| last_indexed | 2024-03-10T00:40:34Z |
| publishDate | 2022-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Biology |
| spelling | doaj.art-ad1b1399078c469e927a4b1e2d0b86ab2023-11-23T15:08:03ZengMDPI AGBiology2079-77372022-09-01119135010.3390/biology11091350SGCNCMI: A New Model Combining Multi-Modal Information to Predict circRNA-Related miRNAs, Diseases and GenesChang-Qing Yu0Xin-Fei Wang1Li-Ping Li2Zhu-Hong You3Wen-Zhun Huang4Yue-Chao Li5Zhong-Hao Ren6Yong-Jian Guan7School of Information Engineering, Xijing University, Xi’an 710123, ChinaSchool of Information Engineering, Xijing University, Xi’an 710123, ChinaCollege of Grassland and Environment Sciences, Xinjiang Agricultural University, Urumqi 830052, ChinaSchool of Computer Science, Northwestern Polytechnical University, Xi’an 710129, ChinaSchool of Information Engineering, Xijing University, Xi’an 710123, ChinaSchool of Information Engineering, Xijing University, Xi’an 710123, ChinaSchool of Information Engineering, Xijing University, Xi’an 710123, ChinaSchool of Information Engineering, Xijing University, Xi’an 710123, ChinaComputational prediction of miRNAs, diseases, and genes associated with circRNAs has important implications for circRNA research, as well as provides a reference for wet experiments to save costs and time. In this study, SGCNCMI, a computational model combining multimodal information and graph convolutional neural networks, combines node similarity to form node information and then predicts associated nodes using GCN with a distributive contribution mechanism. The model can be used not only to predict the molecular level of circRNA–miRNA interactions but also to predict circRNA–cancer and circRNA–gene associations. The AUCs of circRNA—miRNA, circRNA–disease, and circRNA–gene associations in the five-fold cross-validation experiment of SGCNCMI is 89.42%, 84.18%, and 82.44%, respectively. SGCNCMI is one of the few models in this field and achieved the best results. In addition, in our case study, six of the top ten relationship pairs with the highest prediction scores were verified in PubMed.https://www.mdpi.com/2079-7737/11/9/1350circRNA–miRNA interactioncircRNA–cancergraph convolution networkmiRNAk-mer |
| spellingShingle | Chang-Qing Yu Xin-Fei Wang Li-Ping Li Zhu-Hong You Wen-Zhun Huang Yue-Chao Li Zhong-Hao Ren Yong-Jian Guan SGCNCMI: A New Model Combining Multi-Modal Information to Predict circRNA-Related miRNAs, Diseases and Genes Biology circRNA–miRNA interaction circRNA–cancer graph convolution network miRNA k-mer |
| title | SGCNCMI: A New Model Combining Multi-Modal Information to Predict circRNA-Related miRNAs, Diseases and Genes |
| title_full | SGCNCMI: A New Model Combining Multi-Modal Information to Predict circRNA-Related miRNAs, Diseases and Genes |
| title_fullStr | SGCNCMI: A New Model Combining Multi-Modal Information to Predict circRNA-Related miRNAs, Diseases and Genes |
| title_full_unstemmed | SGCNCMI: A New Model Combining Multi-Modal Information to Predict circRNA-Related miRNAs, Diseases and Genes |
| title_short | SGCNCMI: A New Model Combining Multi-Modal Information to Predict circRNA-Related miRNAs, Diseases and Genes |
| title_sort | sgcncmi a new model combining multi modal information to predict circrna related mirnas diseases and genes |
| topic | circRNA–miRNA interaction circRNA–cancer graph convolution network miRNA k-mer |
| url | https://www.mdpi.com/2079-7737/11/9/1350 |
| work_keys_str_mv | AT changqingyu sgcncmianewmodelcombiningmultimodalinformationtopredictcircrnarelatedmirnasdiseasesandgenes AT xinfeiwang sgcncmianewmodelcombiningmultimodalinformationtopredictcircrnarelatedmirnasdiseasesandgenes AT lipingli sgcncmianewmodelcombiningmultimodalinformationtopredictcircrnarelatedmirnasdiseasesandgenes AT zhuhongyou sgcncmianewmodelcombiningmultimodalinformationtopredictcircrnarelatedmirnasdiseasesandgenes AT wenzhunhuang sgcncmianewmodelcombiningmultimodalinformationtopredictcircrnarelatedmirnasdiseasesandgenes AT yuechaoli sgcncmianewmodelcombiningmultimodalinformationtopredictcircrnarelatedmirnasdiseasesandgenes AT zhonghaoren sgcncmianewmodelcombiningmultimodalinformationtopredictcircrnarelatedmirnasdiseasesandgenes AT yongjianguan sgcncmianewmodelcombiningmultimodalinformationtopredictcircrnarelatedmirnasdiseasesandgenes |