  Micro RNAs (miRNA) are a type of non-coding RNA, which are involved in gene
regulation and can be associated with diseases such as cancer, cardiovascular
and neurological diseases. As such, identifying the entire genome of miRNA can
be of great relevance. Since experimental methods for novel precursor miRNA
(pre-miRNA) detection are complex and expensive, computational detection using
ML could be useful. Existing ML methods are often complex black boxes, which do
not create an interpretable structural description of pre-miRNA. In this paper,
we propose a novel framework, which makes use of generative modeling through
Variational Auto-Encoders to uncover the generative factors of pre-miRNA. After
training the VAE, the pre-miRNA description is developed using a decision tree
on the lower dimensional latent space. Applying the framework to miRNA
classification, we obtain a high reconstruction and classification performance,
while also developing an accurate miRNA description.
