Document Details

Document Type : Article In Journal 
Document Title :
Identification of microRNA precursor with the degenerate K-tuple or Kmer strategy.
Identification of microRNA precursor with the degenerate K-tuple or Kmer strategy.
 
Document Language : English 
Abstract : The microRNA (miRNA), a small non-coding RNA molecule, plays an important role in transcriptional and post-transcriptional regulation of gene expression. Its abnormal expression, however, has been observed in many cancers and other disease states, implying that the miRNA molecules are also deeply involved in these diseases, particularly in carcinogenesis. Therefore, it is important for both basic research and miRNA-based therapy to discriminate the real pre-miRNAs from the false ones (such as hairpin sequences with similar stem-loops). Most existing methods in this regard were based on the strategy in which RNA samples were formulated by a vector formed by their Kmer components. But the length of Kmers must be very short; otherwise, the vector's dimension would be extremely large, leading to the "high-dimension disaster" or overfitting problem. Inspired by the concept of "degenerate energy levels" in quantum mechanics, we introduced the "degenerate Kmer" (deKmer) to represent RNA samples. By doing so, not only we can accommodate long-range coupling effects but also we can avoid the high-dimension problem. Rigorous jackknife tests and cross-species experiments indicated that our approach is very promising. It has not escaped our notice that the deKmer approach can also be applied to many other areas of computational biology. A user-friendly web-server for the new predictor has been established at http://bioinformatics.hitsz.edu.cn/miRNA-deKmer/, by which users can easily get their desired results. 
ISSN : 00225193 
Journal Name : J Theor Biol 
Volume : 385 
Issue Number : 1 
Publishing Year : 1436 AH
2015 AD 
Article Type : Article 
Added Date : Sunday, April 24, 2016 

Researchers

Researcher Name (Arabic)Researcher Name (English)Researcher TypeDr GradeEmail
Bin LiuLiu, Bin Investigator bliu@insun.hit.edu.cn
Longyun FangFang, Longyun Researcher dragoncloudest@gmail.com
Shanyi WangWang, Shanyi Researcher wangshanyiwsy@gmail.com
Xiaolong WangWang, Xiaolong Researcher wangxl@insun.hit.edu.cn
Hongtao LiLi, Hongtao Researcher lht760@126.com
Kuo-Chen ChouChou, Kuo-Chen Researcher kcchou@gordonlifescience.org

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