Long Noncoding RNA and Protein Interactions: From Experimental Results to Computational Models Based on Network Methods
Non-coding RNAs with a length of more than 200 nucleotides are long non-coding RNAs (lncRNAs), which have gained tremendous attention in recent decades. Many studies have confirmed that lncRNAs have important influence in post-transcriptional gene regulation; for example, lncRNAs affect the stabilit...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2019-03-01
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| Series: | International Journal of Molecular Sciences |
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| Online Access: | http://www.mdpi.com/1422-0067/20/6/1284 |
| _version_ | 1811325592456396800 |
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| author | Hui Zhang Yanchun Liang Siyu Han Cheng Peng Ying Li |
| author_facet | Hui Zhang Yanchun Liang Siyu Han Cheng Peng Ying Li |
| author_sort | Hui Zhang |
| collection | DOAJ |
| description | Non-coding RNAs with a length of more than 200 nucleotides are long non-coding RNAs (lncRNAs), which have gained tremendous attention in recent decades. Many studies have confirmed that lncRNAs have important influence in post-transcriptional gene regulation; for example, lncRNAs affect the stability and translation of splicing factor proteins. The mutations and malfunctions of lncRNAs are closely related to human disorders. As lncRNAs interact with a variety of proteins, predicting the interaction between lncRNAs and proteins is a significant way to depth exploration functions and enrich annotations of lncRNAs. Experimental approaches for lncRNA–protein interactions are expensive and time-consuming. Computational approaches to predict lncRNA–protein interactions can be grouped into two broad categories. The first category is based on sequence, structural information and physicochemical property. The second category is based on network method through fusing heterogeneous data to construct lncRNA related heterogeneous network. The network-based methods can capture the implicit feature information in the topological structure of related biological heterogeneous networks containing lncRNAs, which is often ignored by sequence-based methods. In this paper, we summarize and discuss the materials, interaction score calculation algorithms, advantages and disadvantages of state-of-the-art algorithms of lncRNA–protein interaction prediction based on network methods to assist researchers in selecting a suitable method for acquiring more dependable results. All the related different network data are also collected and processed in convenience of users, and are available at https://github.com/HAN-Siyu/APINet/. |
| first_indexed | 2024-04-13T14:36:10Z |
| format | Article |
| id | doaj.art-8820118f687142d0833e226a9ce53a24 |
| institution | Directory Open Access Journal |
| issn | 1422-0067 |
| language | English |
| last_indexed | 2024-04-13T14:36:10Z |
| publishDate | 2019-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | International Journal of Molecular Sciences |
| spelling | doaj.art-8820118f687142d0833e226a9ce53a242022-12-22T02:43:02ZengMDPI AGInternational Journal of Molecular Sciences1422-00672019-03-01206128410.3390/ijms20061284ijms20061284Long Noncoding RNA and Protein Interactions: From Experimental Results to Computational Models Based on Network MethodsHui Zhang0Yanchun Liang1Siyu Han2Cheng Peng3Ying Li4College of Computer Science and Technology, Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun 130012, ChinaCollege of Computer Science and Technology, Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun 130012, ChinaCollege of Computer Science and Technology, Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun 130012, ChinaCollege of Computer Science and Technology, Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun 130012, ChinaCollege of Computer Science and Technology, Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun 130012, ChinaNon-coding RNAs with a length of more than 200 nucleotides are long non-coding RNAs (lncRNAs), which have gained tremendous attention in recent decades. Many studies have confirmed that lncRNAs have important influence in post-transcriptional gene regulation; for example, lncRNAs affect the stability and translation of splicing factor proteins. The mutations and malfunctions of lncRNAs are closely related to human disorders. As lncRNAs interact with a variety of proteins, predicting the interaction between lncRNAs and proteins is a significant way to depth exploration functions and enrich annotations of lncRNAs. Experimental approaches for lncRNA–protein interactions are expensive and time-consuming. Computational approaches to predict lncRNA–protein interactions can be grouped into two broad categories. The first category is based on sequence, structural information and physicochemical property. The second category is based on network method through fusing heterogeneous data to construct lncRNA related heterogeneous network. The network-based methods can capture the implicit feature information in the topological structure of related biological heterogeneous networks containing lncRNAs, which is often ignored by sequence-based methods. In this paper, we summarize and discuss the materials, interaction score calculation algorithms, advantages and disadvantages of state-of-the-art algorithms of lncRNA–protein interaction prediction based on network methods to assist researchers in selecting a suitable method for acquiring more dependable results. All the related different network data are also collected and processed in convenience of users, and are available at https://github.com/HAN-Siyu/APINet/.http://www.mdpi.com/1422-0067/20/6/1284lncRNA–protein interaction predictioncomputational modelbiological network sciencemachine learning |
| spellingShingle | Hui Zhang Yanchun Liang Siyu Han Cheng Peng Ying Li Long Noncoding RNA and Protein Interactions: From Experimental Results to Computational Models Based on Network Methods International Journal of Molecular Sciences lncRNA–protein interaction prediction computational model biological network science machine learning |
| title | Long Noncoding RNA and Protein Interactions: From Experimental Results to Computational Models Based on Network Methods |
| title_full | Long Noncoding RNA and Protein Interactions: From Experimental Results to Computational Models Based on Network Methods |
| title_fullStr | Long Noncoding RNA and Protein Interactions: From Experimental Results to Computational Models Based on Network Methods |
| title_full_unstemmed | Long Noncoding RNA and Protein Interactions: From Experimental Results to Computational Models Based on Network Methods |
| title_short | Long Noncoding RNA and Protein Interactions: From Experimental Results to Computational Models Based on Network Methods |
| title_sort | long noncoding rna and protein interactions from experimental results to computational models based on network methods |
| topic | lncRNA–protein interaction prediction computational model biological network science machine learning |
| url | http://www.mdpi.com/1422-0067/20/6/1284 |
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