Abstract
MicroRNAs (miRNA's) constitute a large family of non coding RNAs that function to regulate gene expression. Wet lab experiments usually used to classify the miRNA of plants and animals are highly expensive, labor intensive and time consuming. Thus there arises a need for computational approach for classification of plant and animal miRNA. These computational approaches are fast and economical as compared to wet lab techniques. Here a machine learning approach is used to classify miRNA of HIV, plants and animals. The new SVM learning algorithm called Weka LibSVM has been used for classification of plant and animal and HIVmiRNA. The model has been tested on available data and it gives results with 95% accuracy.
Highlights
MicroRNAs are small RNAs of 21–25 nucleotides that regulate cellular gene expression at the post-transcriptional level. miRNA’s are derived from the maturation by cellular RNases III of imperfect stem loop structures of ~ 70 nucleotides
Nucleotide positions in the pNL4-3 genome are presented in the right column.It was matched deduced mature miRNA sequences from these 5 pre-miRNA against a database of 3' untranslated sequences (UTR) from the human genome
These searches revealed a large number of cellular transcripts that could potentially be targeted by these viral miRNA sequences [1]
Summary
MicroRNAs (miRNA’s) are small RNAs of 21–25 nucleotides that regulate cellular gene expression at the post-transcriptional level. miRNA’s are derived from the maturation by cellular RNases III of imperfect stem loop structures of ~ 70 nucleotides. Nucleotide positions (where 1 is the initiation of transcription) in the pNL4-3 genome are presented in the right column.It was matched deduced mature miRNA sequences from these 5 pre-miRNA against a database of 3' untranslated sequences (UTR) from the human genome These searches revealed a large number of cellular transcripts that could potentially be targeted by these viral miRNA (vmiRNA) sequences [1]. The goal of SVM is to produce a model which predicts target value of data instances in the testing set which are given only the attributes [4]. Size of fold back loop is greater than 100 for plant with variations till 303 nucleotides and less than 100 nucleotides for animals [13] With these characteristics we have trained the WEKA classifier and the values we get are given in the table 2
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