| Chapter title |
Computationally Modeling ncRNA-ncRNA Crosstalk
|
|---|---|
| Chapter number | 8 |
| Book title |
Non-coding RNAs in Complex Diseases
|
| Published in |
Advances in experimental medicine and biology, September 2018
|
| DOI | 10.1007/978-981-13-0719-5_8 |
| Pubmed ID | |
| Book ISBNs |
978-9-81-130718-8, 978-9-81-130719-5
|
| Authors |
Juan Xu, Jing Bai, Jun Xiao, Xu, Juan, Bai, Jing, Xiao, Jun |
| Abstract |
Our understanding of complex gene regulatory networks have been improved by the discovery of ncRNA-ncRNA crosstalk in normal and disease-specific physiological conditions. Previous studies have proposed numerous approaches for constructing ncRNA-ncRNA networks via ncRNA-mRNA regulation, functional information, or phenomics alone, or by combining heterogeneous data. Furthermore, it has been shown that ncRNA-ncRNA crosstalk can be rewired in different tissues or specific diseases. Therefore, it is necessary to integrate transcriptome data to construct context-specific ncRNA-ncRNA networks. In this chapter, we elucidated the commonly used ncRNA-ncRNA network modeling methods, and highlighted the need to integrate heterogeneous multi-mics data. Finally, we suggest future directions for studies of ncRNAs crosstalk. This comprehensive description and discussion elucidated in this chapter will provide constructive insights into ncRNA-ncRNA crosstalk. |
X Demographics
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| Country | Count | As % |
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| United States | 1 | 50% |
| Unknown | 1 | 50% |
Demographic breakdown
| Type | Count | As % |
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| Members of the public | 2 | 100% |
Mendeley readers
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 4 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 1 | 25% |
| Professor > Associate Professor | 1 | 25% |
| Student > Bachelor | 1 | 25% |
| Unknown | 1 | 25% |
| Readers by discipline | Count | As % |
|---|---|---|
| Mathematics | 1 | 25% |
| Agricultural and Biological Sciences | 1 | 25% |
| Neuroscience | 1 | 25% |
| Unknown | 1 | 25% |