Description Generation Using Variational Auto-Encoders for Precursor microRNA
Micro RNAs (miRNA) are a type of non-coding RNA 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 (...
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MDPI AG
2024-10-01
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| Online Access: | https://www.mdpi.com/1099-4300/26/11/921 |
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| author | Marko Petković Vlado Menkovski |
| author_facet | Marko Petković Vlado Menkovski |
| author_sort | Marko Petković |
| collection | DOAJ |
| description | Micro RNAs (miRNA) are a type of non-coding RNA 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 Machine Learning (ML) could be useful. Existing ML methods are often complex black boxes that do not create an interpretable structural description of pre-miRNA. In this paper, we propose a novel framework that 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. |
| format | Article |
| id | doaj-art-807c554dcd614843a5aa5c9dd9817244 |
| institution | OA Journals |
| issn | 1099-4300 |
| language | English |
| publishDate | 2024-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Entropy |
| spelling | doaj-art-807c554dcd614843a5aa5c9dd98172442025-08-20T02:28:09ZengMDPI AGEntropy1099-43002024-10-01261192110.3390/e26110921Description Generation Using Variational Auto-Encoders for Precursor microRNAMarko Petković0Vlado Menkovski1Department of Applied Physics and Science Education, Eindhoven University of Technology, 5612AZ Eindhoven, The NetherlandsEindhoven Artificial Intelligence Systems Institute, 5612AZ Eindhoven, The NetherlandsMicro RNAs (miRNA) are a type of non-coding RNA 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 Machine Learning (ML) could be useful. Existing ML methods are often complex black boxes that do not create an interpretable structural description of pre-miRNA. In this paper, we propose a novel framework that 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.https://www.mdpi.com/1099-4300/26/11/921generative modelsinterpretabilitydescription generationmicroRNA |
| spellingShingle | Marko Petković Vlado Menkovski Description Generation Using Variational Auto-Encoders for Precursor microRNA Entropy generative models interpretability description generation microRNA |
| title | Description Generation Using Variational Auto-Encoders for Precursor microRNA |
| title_full | Description Generation Using Variational Auto-Encoders for Precursor microRNA |
| title_fullStr | Description Generation Using Variational Auto-Encoders for Precursor microRNA |
| title_full_unstemmed | Description Generation Using Variational Auto-Encoders for Precursor microRNA |
| title_short | Description Generation Using Variational Auto-Encoders for Precursor microRNA |
| title_sort | description generation using variational auto encoders for precursor microrna |
| topic | generative models interpretability description generation microRNA |
| url | https://www.mdpi.com/1099-4300/26/11/921 |
| work_keys_str_mv | AT markopetkovic descriptiongenerationusingvariationalautoencodersforprecursormicrorna AT vladomenkovski descriptiongenerationusingvariationalautoencodersforprecursormicrorna |