Description Generation using Variational Auto-Encoders for precursor microRNA
This work addresses the need for interpretable computational methods in bioinformatics for pre-miRNA identification, which is incremental as it builds on existing ML approaches by adding description generation.
The paper tackles the problem of generating interpretable structural descriptions for precursor microRNAs (pre-miRNAs) to aid in computational detection, using a framework based on Variational Auto-Encoders and decision trees, resulting in high reconstruction and classification performance.
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.