Chapter title |
Identifying Interactions Between Long Noncoding RNAs and Diseases Based on Computational Methods
|
---|---|
Chapter number | 12 |
Book title |
Computational Systems Biology
|
Published in |
Methods in molecular biology, January 2018
|
DOI | 10.1007/978-1-4939-7717-8_12 |
Pubmed ID | |
Book ISBNs |
978-1-4939-7716-1, 978-1-4939-7717-8
|
Authors |
Wei Lan, Liyu Huang, Dehuan Lai, Qingfeng Chen, Lan, Wei, Huang, Liyu, Lai, Dehuan, Chen, Qingfeng |
Abstract |
With the development and improvement of next-generation sequencing technology, a great number of noncoding RNAs have been discovered. Long noncoding RNAs (lncRNAs) are the biggest kind of noncoding RNAs with more than 200 nt nucleotides in length. There are increasing evidences showing that lncRNAs play key roles in many biological processes. Therefore, the mutation and dysregulation of lncRNAs have close association with a number of complex human diseases. Identifying the most likely interaction between lncRNAs and diseases becomes a fundamental challenge in human health. A common view is that lncRNAs with similar function tend to be related to phenotypic similar diseases. In this chapter, we firstly introduce the concept of lncRNA, their biological features, and available data resources. Further, the recent computational approaches are explored to identify interactions between long noncoding RNAs and diseases, including their advantages and disadvantages. The key issues and potential future works of predicting interactions between long noncoding RNAs and diseases are also discussed. |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
Unknown | 18 | 100% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Researcher | 3 | 17% |
Student > Bachelor | 2 | 11% |
Student > Doctoral Student | 2 | 11% |
Student > Master | 2 | 11% |
Student > Ph. D. Student | 2 | 11% |
Other | 0 | 0% |
Unknown | 7 | 39% |
Readers by discipline | Count | As % |
---|---|---|
Biochemistry, Genetics and Molecular Biology | 7 | 39% |
Agricultural and Biological Sciences | 2 | 11% |
Mathematics | 1 | 6% |
Medicine and Dentistry | 1 | 6% |
Unknown | 7 | 39% |