Representing Short Read Clusters As Feature Vectors- Which Properties To Include? · Biostar

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Representing Short Read Clusters As Feature Vectors- Which Properties To Include?

I want to train an SVM to recognize piRNA-clusters as opposed to clusters of miRNA, tRNA, mRNA.

I have found a few thousand clusters which are almost certainly piRNA clusters. I want to represent those clusters as feature vectors, to enable an SVM to find other likely candidates for piRNA clusters. However, the only features I can think of including in such a vector is: the length of the cluster (nb. of basepairs) and the number of reads in the clusters.

Are there other properties I should consider adding?

short read

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updated 12.7 years ago by Biostar

• written 13.7 years ago by Click downvote

2 answers

maybe binucleotide, trinucleotide frequencies? (k-mers frequencies) and their ordering?

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written 13.7 years ago by Pavel Senin

Will have to look those up. Thanks!

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written 13.7 years ago by Click downvote

Time ago I wrote some complexity and composition function in Perl, check: https://github.com/caballero/SeqComplex

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written 13.7 years ago by JC

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