Next Article in Journal
Obatoclax, a BH3 Mimetic, Enhances Cisplatin-Induced Apoptosis and Decreases the Clonogenicity of Muscle Invasive Bladder Cancer Cells via Mechanisms That Involve the Inhibition of Pro-Survival Molecules as Well as Cell Cycle Regulators
Next Article in Special Issue
Analysis of Expression Pattern of snoRNAs in Different Cancer Types with Machine Learning Algorithms
Previous Article in Journal
Killing Mechanisms of Chimeric Antigen Receptor (CAR) T Cells
Previous Article in Special Issue
RPITER: A Hierarchical Deep Learning Framework for ncRNA–Protein Interaction Prediction
Article Menu
Issue 6 (March-2) cover image

Export Article

Open AccessArticle

Long Noncoding RNA and Protein Interactions: From Experimental Results to Computational Models Based on Network Methods

1
College of Computer Science and Technology, Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun 130012, China
2
Zhuhai Laboratory of Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education, Zhuhai College of Jilin University, Zhuhai 519041, China
*
Author to whom correspondence should be addressed.
Int. J. Mol. Sci. 2019, 20(6), 1284; https://doi.org/10.3390/ijms20061284
Received: 27 January 2019 / Revised: 9 March 2019 / Accepted: 11 March 2019 / Published: 14 March 2019
(This article belongs to the Special Issue Computational Models in Non-Coding RNA and Human Disease)
  |  
PDF [3024 KB, uploaded 14 March 2019]
  |  

Abstract

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/. View Full-Text
Keywords: lncRNA–protein interaction prediction; computational model; biological network science; machine learning lncRNA–protein interaction prediction; computational model; biological network science; machine learning
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
SciFeed

Share & Cite This Article

MDPI and ACS Style

Zhang, H.; Liang, Y.; Han, S.; Peng, C.; Li, Y. Long Noncoding RNA and Protein Interactions: From Experimental Results to Computational Models Based on Network Methods. Int. J. Mol. Sci. 2019, 20, 1284.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Int. J. Mol. Sci. EISSN 1422-0067 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top