Construction and Analysis of Molecular Association Network by Combining Behavior Representation and Node Attributes
A key aim of post-genomic biomedical research is to systematically understand and model complex biomolecular activities based on a systematic perspective. Biomolecular interactions are widespread and interrelated, multiple biomolecules coordinate to sustain life activities, any disturbance of these...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2019-11-01
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| Series: | Frontiers in Genetics |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/article/10.3389/fgene.2019.01106/full |
| _version_ | 1818358852602560512 |
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| author | Hai-Cheng Yi Hai-Cheng Yi Zhu-Hong You Zhen-Hao Guo |
| author_facet | Hai-Cheng Yi Hai-Cheng Yi Zhu-Hong You Zhen-Hao Guo |
| author_sort | Hai-Cheng Yi |
| collection | DOAJ |
| description | A key aim of post-genomic biomedical research is to systematically understand and model complex biomolecular activities based on a systematic perspective. Biomolecular interactions are widespread and interrelated, multiple biomolecules coordinate to sustain life activities, any disturbance of these complex connections can lead to abnormal of life activities or complex diseases. However, many existing researches usually only focus on individual intermolecular interactions. In this work, we revealed, constructed, and analyzed a large-scale molecular association network of multiple biomolecules in human by integrating associations among lncRNAs, miRNAs, proteins, drugs, and diseases, in which various associations are interconnected and any type of associations can be predicted. We propose Molecular Association Network (MAN)–High-Order Proximity preserved Embedding (HOPE), a novel network representation learning based method to fully exploit latent feature of biomolecules to accurately predict associations between molecules. More specifically, network representation learning algorithm HOPE was applied to learn behavior feature of nodes in the association network. Attribute features of nodes were also adopted. Then, a machine learning model CatBoost was trained to predict potential association between any nodes. The performance of our method was evaluated under five-fold cross validation. A case study to predict miRNA-disease associations was also conducted to verify the prediction capability. MAN-HOPE achieves high accuracy of 93.3% and area under the receiver operating characteristic curve of 0.9793. The experimental results demonstrate the novelty of our systematic understanding of the intermolecular associations, and enable systematic exploration of the landscape of molecular interactions that shape specialized cellular functions. |
| first_indexed | 2024-12-13T20:35:35Z |
| format | Article |
| id | doaj.art-01f56684fd7342c59909bd85e6a22ae6 |
| institution | Directory Open Access Journal |
| issn | 1664-8021 |
| language | English |
| last_indexed | 2024-12-13T20:35:35Z |
| publishDate | 2019-11-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Genetics |
| spelling | doaj.art-01f56684fd7342c59909bd85e6a22ae62022-12-21T23:32:17ZengFrontiers Media S.A.Frontiers in Genetics1664-80212019-11-011010.3389/fgene.2019.01106490501Construction and Analysis of Molecular Association Network by Combining Behavior Representation and Node AttributesHai-Cheng Yi0Hai-Cheng Yi1Zhu-Hong You2Zhen-Hao Guo3Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Urumqi, ChinaUniversity of Chinese Academy of Sciences, Beijing, ChinaXinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Urumqi, ChinaXinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Urumqi, ChinaA key aim of post-genomic biomedical research is to systematically understand and model complex biomolecular activities based on a systematic perspective. Biomolecular interactions are widespread and interrelated, multiple biomolecules coordinate to sustain life activities, any disturbance of these complex connections can lead to abnormal of life activities or complex diseases. However, many existing researches usually only focus on individual intermolecular interactions. In this work, we revealed, constructed, and analyzed a large-scale molecular association network of multiple biomolecules in human by integrating associations among lncRNAs, miRNAs, proteins, drugs, and diseases, in which various associations are interconnected and any type of associations can be predicted. We propose Molecular Association Network (MAN)–High-Order Proximity preserved Embedding (HOPE), a novel network representation learning based method to fully exploit latent feature of biomolecules to accurately predict associations between molecules. More specifically, network representation learning algorithm HOPE was applied to learn behavior feature of nodes in the association network. Attribute features of nodes were also adopted. Then, a machine learning model CatBoost was trained to predict potential association between any nodes. The performance of our method was evaluated under five-fold cross validation. A case study to predict miRNA-disease associations was also conducted to verify the prediction capability. MAN-HOPE achieves high accuracy of 93.3% and area under the receiver operating characteristic curve of 0.9793. The experimental results demonstrate the novelty of our systematic understanding of the intermolecular associations, and enable systematic exploration of the landscape of molecular interactions that shape specialized cellular functions.https://www.frontiersin.org/article/10.3389/fgene.2019.01106/fulldata analysisnetwork biologymachine learningassociation predictiongraph embeddingmiRNA-disease association |
| spellingShingle | Hai-Cheng Yi Hai-Cheng Yi Zhu-Hong You Zhen-Hao Guo Construction and Analysis of Molecular Association Network by Combining Behavior Representation and Node Attributes Frontiers in Genetics data analysis network biology machine learning association prediction graph embedding miRNA-disease association |
| title | Construction and Analysis of Molecular Association Network by Combining Behavior Representation and Node Attributes |
| title_full | Construction and Analysis of Molecular Association Network by Combining Behavior Representation and Node Attributes |
| title_fullStr | Construction and Analysis of Molecular Association Network by Combining Behavior Representation and Node Attributes |
| title_full_unstemmed | Construction and Analysis of Molecular Association Network by Combining Behavior Representation and Node Attributes |
| title_short | Construction and Analysis of Molecular Association Network by Combining Behavior Representation and Node Attributes |
| title_sort | construction and analysis of molecular association network by combining behavior representation and node attributes |
| topic | data analysis network biology machine learning association prediction graph embedding miRNA-disease association |
| url | https://www.frontiersin.org/article/10.3389/fgene.2019.01106/full |
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