Literature DB >> 29536445

Identifying Interactions Between Long Noncoding RNAs and Diseases Based on Computational Methods.

Wei Lan1, Liyu Huang2, Dehuan Lai1, Qingfeng Chen3,4.   

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.

Entities:  

Keywords:  Biological networks; Heterogeneous data fusion; Human disease; Long noncoding RNA; Machine learning

Mesh:

Substances:

Year:  2018        PMID: 29536445     DOI: 10.1007/978-1-4939-7717-8_12

Source DB:  PubMed          Journal:  Methods Mol Biol        ISSN: 1064-3745


  4 in total

1.  CNNDLP: A Method Based on Convolutional Autoencoder and Convolutional Neural Network with Adjacent Edge Attention for Predicting lncRNA-Disease Associations.

Authors:  Ping Xuan; Nan Sheng; Tiangang Zhang; Yong Liu; Yahong Guo
Journal:  Int J Mol Sci       Date:  2019-08-30       Impact factor: 5.923

2.  CircR2Cancer: a manually curated database of associations between circRNAs and cancers.

Authors:  Wei Lan; Mingrui Zhu; Qingfeng Chen; Baoshan Chen; Jin Liu; Min Li; Yi-Ping Phoebe Chen
Journal:  Database (Oxford)       Date:  2020-01-01       Impact factor: 3.451

3.  Inferring microRNA-Environmental Factor Interactions Based on Multiple Biological Information Fusion.

Authors:  Haiqiong Luo; Wei Lan; Qingfeng Chen; Zhiqiang Wang; Zhixian Liu; Xiaofeng Yue; Lingzhi Zhu
Journal:  Molecules       Date:  2018-09-24       Impact factor: 4.411

4.  In silico identification of conserved miRNAs and their selective target gene prediction in indicine (Bos indicus) cattle.

Authors:  Quratulain Hanif; Muhammad Farooq; Imran Amin; Shahid Mansoor; Yi Zhang; Qaiser Mahmood Khan
Journal:  PLoS One       Date:  2018-10-26       Impact factor: 3.240

  4 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.