Description Generation using Variational Auto-Encoders for precursor microRNA

• URL: http://arxiv.org/abs/2311.17970v1
• Date: Wed, 29 Nov 2023 15:41:45 GMT
• Title: Description Generation using Variational Auto-Encoders for precursor microRNA
• Authors: Marko Petkovi\'c, Vlado Menkovski
• Abstract summary: We propose a novel framework, which makes use of generative modeling through Vari Auto-Encoders to uncover latent factors of pre-miRNA. Applying the framework to classification, we obtain a high reconstruction and classification performance, while also developing an accurate description.
• Score: 5.6710852973206105
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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