Small non-coding RNA genes have been concerned as an important field of life sciences in
recent years. It plays important regulatory roles in cellular processes. However, the prediction of noncoding
RNA genes is a great challenge, because non-coding RNAs have a small size, are not translated
into proteins and show variable stability. In this paper, we propose an improved inter-nucleotide distances
model as sequence characteristics, and combine with support vector machines (SVM) to predict small
non-coding RNA in bacterial genomes. The prediction result of the mixed bacterial ncRNA is 95.38%,
which shows that our method can effectively predict bacterial ncRNAs.
Keywords: Small non-coding RNA, inter-nucleotide distances, prediction, support vector machines, machine learning.
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