SCCPMD: Probability matrix decomposition method subject to corrected similarity constraints for inferring long non-coding RNA–disease associations
Accumulating evidence has demonstrated various associations of long non-coding RNAs (lncRNAs) with human diseases, such as abnormal expression due to microbial influences that cause disease. Gaining a deeper understanding of lncRNA–disease associations is essential for disease diagnosis, treatment,...
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| Veröffentlicht in: | Frontiers in microbiology Jg. 13; S. 1093615 |
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11.01.2023
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| Abstract | Accumulating evidence has demonstrated various associations of long non-coding RNAs (lncRNAs) with human diseases, such as abnormal expression due to microbial influences that cause disease. Gaining a deeper understanding of lncRNA–disease associations is essential for disease diagnosis, treatment, and prevention. In recent years, many matrix decomposition methods have also been used to predict potential lncRNA-disease associations. However, these methods do not consider the use of microbe-disease association information to enrich disease similarity, and also do not make more use of similarity information in the decomposition process. To address these issues, we here propose a correction-based similarity-constrained probability matrix decomposition method (SCCPMD) to predict lncRNA–disease associations. The microbe-disease associations are first used to enrich the disease semantic similarity matrix, and then the logistic function is used to correct the lncRNA and disease similarity matrix, and then these two corrected similarity matrices are added to the probability matrix decomposition as constraints to finally predict the potential lncRNA–disease associations. The experimental results show that SCCPMD outperforms the five advanced comparison algorithms. In addition, SCCPMD demonstrated excellent prediction performance in a case study for breast cancer, lung cancer, and renal cell carcinoma, with prediction accuracy reaching 80, 100, and 100%, respectively. Therefore, SCCPMD shows excellent predictive performance in identifying unknown lncRNA–disease associations. |
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| AbstractList | Accumulating evidence has demonstrated various associations of long non-coding RNAs (lncRNAs) with human diseases, such as abnormal expression due to microbial influences that cause disease. Gaining a deeper understanding of lncRNA–disease associations is essential for disease diagnosis, treatment, and prevention. In recent years, many matrix decomposition methods have also been used to predict potential lncRNA-disease associations. However, these methods do not consider the use of microbe-disease association information to enrich disease similarity, and also do not make more use of similarity information in the decomposition process. To address these issues, we here propose a correction-based similarity-constrained probability matrix decomposition method (SCCPMD) to predict lncRNA–disease associations. The microbe-disease associations are first used to enrich the disease semantic similarity matrix, and then the logistic function is used to correct the lncRNA and disease similarity matrix, and then these two corrected similarity matrices are added to the probability matrix decomposition as constraints to finally predict the potential lncRNA–disease associations. The experimental results show that SCCPMD outperforms the five advanced comparison algorithms. In addition, SCCPMD demonstrated excellent prediction performance in a case study for breast cancer, lung cancer, and renal cell carcinoma, with prediction accuracy reaching 80, 100, and 100%, respectively. Therefore, SCCPMD shows excellent predictive performance in identifying unknown lncRNA–disease associations. Accumulating evidence has demonstrated various associations of long non-coding RNAs (lncRNAs) with human diseases, such as abnormal expression due to microbial influences that cause disease. Gaining a deeper understanding of lncRNA-disease associations is essential for disease diagnosis, treatment, and prevention. In recent years, many matrix decomposition methods have also been used to predict potential lncRNA-disease associations. However, these methods do not consider the use of microbe-disease association information to enrich disease similarity, and also do not make more use of similarity information in the decomposition process. To address these issues, we here propose a correction-based similarity-constrained probability matrix decomposition method (SCCPMD) to predict lncRNA-disease associations. The microbe-disease associations are first used to enrich the disease semantic similarity matrix, and then the logistic function is used to correct the lncRNA and disease similarity matrix, and then these two corrected similarity matrices are added to the probability matrix decomposition as constraints to finally predict the potential lncRNA-disease associations. The experimental results show that SCCPMD outperforms the five advanced comparison algorithms. In addition, SCCPMD demonstrated excellent prediction performance in a case study for breast cancer, lung cancer, and renal cell carcinoma, with prediction accuracy reaching 80, 100, and 100%, respectively. Therefore, SCCPMD shows excellent predictive performance in identifying unknown lncRNA-disease associations.Accumulating evidence has demonstrated various associations of long non-coding RNAs (lncRNAs) with human diseases, such as abnormal expression due to microbial influences that cause disease. Gaining a deeper understanding of lncRNA-disease associations is essential for disease diagnosis, treatment, and prevention. In recent years, many matrix decomposition methods have also been used to predict potential lncRNA-disease associations. However, these methods do not consider the use of microbe-disease association information to enrich disease similarity, and also do not make more use of similarity information in the decomposition process. To address these issues, we here propose a correction-based similarity-constrained probability matrix decomposition method (SCCPMD) to predict lncRNA-disease associations. The microbe-disease associations are first used to enrich the disease semantic similarity matrix, and then the logistic function is used to correct the lncRNA and disease similarity matrix, and then these two corrected similarity matrices are added to the probability matrix decomposition as constraints to finally predict the potential lncRNA-disease associations. The experimental results show that SCCPMD outperforms the five advanced comparison algorithms. In addition, SCCPMD demonstrated excellent prediction performance in a case study for breast cancer, lung cancer, and renal cell carcinoma, with prediction accuracy reaching 80, 100, and 100%, respectively. Therefore, SCCPMD shows excellent predictive performance in identifying unknown lncRNA-disease associations. |
| Author | Chen, Ruibin Zhu, Yinting Jing, Huaiguo Lin, Lieqing Chen, Langcheng Zou, Minqing Xie, Weijie |
| AuthorAffiliation | 1 Center of Campus Network & Modern Educational Technology, Guangdong University of Technology , Guangzhou , China 2 School of Computer, Guangdong University of Technology , Guangzhou , China 4 Department of Experiment Teaching, Guangdong University of Technology , Guangzhou , China 3 Sports Department, Guangdong University of Technology , Guangzhou , China |
| AuthorAffiliation_xml | – name: 1 Center of Campus Network & Modern Educational Technology, Guangdong University of Technology , Guangzhou , China – name: 4 Department of Experiment Teaching, Guangdong University of Technology , Guangzhou , China – name: 2 School of Computer, Guangdong University of Technology , Guangzhou , China – name: 3 Sports Department, Guangdong University of Technology , Guangzhou , China |
| Author_xml | – sequence: 1 givenname: Lieqing surname: Lin fullname: Lin, Lieqing – sequence: 2 givenname: Ruibin surname: Chen fullname: Chen, Ruibin – sequence: 3 givenname: Yinting surname: Zhu fullname: Zhu, Yinting – sequence: 4 givenname: Weijie surname: Xie fullname: Xie, Weijie – sequence: 5 givenname: Huaiguo surname: Jing fullname: Jing, Huaiguo – sequence: 6 givenname: Langcheng surname: Chen fullname: Chen, Langcheng – sequence: 7 givenname: Minqing surname: Zou fullname: Zou, Minqing |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/36713213$$D View this record in MEDLINE/PubMed |
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| Copyright | Copyright © 2023 Lin, Chen, Zhu, Xie, Jing, Chen and Zou. Copyright © 2023 Lin, Chen, Zhu, Xie, Jing, Chen and Zou. 2023 Lin, Chen, Zhu, Xie, Jing, Chen and Zou |
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| Keywords | similarity correction associations prediction disease lncRNA-long noncoding RNA constraint probability matrix decomposition |
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| License | Copyright © 2023 Lin, Chen, Zhu, Xie, Jing, Chen and Zou. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
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| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 Edited by: Qi Zhao, University of Science and Technology Liaoning, China This article was submitted to Systems Microbiology, a section of the journal Frontiers in Microbiology Reviewed by: Chun-Chun Wang, China University of Mining and Technology, China; Li Zhang, Liaoning University, China |
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| Title | SCCPMD: Probability matrix decomposition method subject to corrected similarity constraints for inferring long non-coding RNA–disease associations |
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