Identification of miRNA-Small Molecule Associations by Continuous Feature Representation Using Auto-Encoders
MicroRNAs (miRNAs) are short non-coding RNAs that play important roles in the body and affect various diseases, including cancers. Controlling miRNAs with small molecules is studied herein to provide new drug repurposing perspectives for miRNA-related diseases. Experimental methods are time- and eff...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2021-12-01
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| Series: | Pharmaceutics |
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| Online Access: | https://www.mdpi.com/1999-4923/14/1/3 |
| _version_ | 1827663522938486784 |
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| author | Ibrahim Abdelbaky Hilal Tayara Kil To Chong |
| author_facet | Ibrahim Abdelbaky Hilal Tayara Kil To Chong |
| author_sort | Ibrahim Abdelbaky |
| collection | DOAJ |
| description | MicroRNAs (miRNAs) are short non-coding RNAs that play important roles in the body and affect various diseases, including cancers. Controlling miRNAs with small molecules is studied herein to provide new drug repurposing perspectives for miRNA-related diseases. Experimental methods are time- and effort-consuming, so computational techniques have been applied, relying mostly on biological feature similarities and a network-based scheme to infer new miRNA–small molecule associations. Collecting such features is time-consuming and may be impractical. Here we suggest an alternative method of similarity calculation, representing miRNAs and small molecules through continuous feature representation. This representation is learned by the proposed deep learning auto-encoder architecture. Our suggested representation was compared to previous works and achieved comparable results using 5-fold cross validation (92% identified within top 25% predictions), and better predictions for most of the case studies (avg. of 31% vs. 25% identified within the top 25% of predictions). The results proved the effectiveness of our proposed method to replace previous time- and effort-consuming methods. |
| first_indexed | 2024-03-10T00:43:59Z |
| format | Article |
| id | doaj.art-3a9577e0c82c4c2aa8d00544471b1d6b |
| institution | Directory Open Access Journal |
| issn | 1999-4923 |
| language | English |
| last_indexed | 2024-03-10T00:43:59Z |
| publishDate | 2021-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Pharmaceutics |
| spelling | doaj.art-3a9577e0c82c4c2aa8d00544471b1d6b2023-11-23T15:02:15ZengMDPI AGPharmaceutics1999-49232021-12-01141310.3390/pharmaceutics14010003Identification of miRNA-Small Molecule Associations by Continuous Feature Representation Using Auto-EncodersIbrahim Abdelbaky0Hilal Tayara1Kil To Chong2Artificial Intelligence Department, Faculty of Computers and Artificial Intelligence, Benha University, Banha 13518, EgyptSchool of International Engineering and Science, Jeonbuk National University, Jeonju 54896, KoreaDepartment of Electronics and Information Engineering, Jeonbuk National University, Jeonju 54896, KoreaMicroRNAs (miRNAs) are short non-coding RNAs that play important roles in the body and affect various diseases, including cancers. Controlling miRNAs with small molecules is studied herein to provide new drug repurposing perspectives for miRNA-related diseases. Experimental methods are time- and effort-consuming, so computational techniques have been applied, relying mostly on biological feature similarities and a network-based scheme to infer new miRNA–small molecule associations. Collecting such features is time-consuming and may be impractical. Here we suggest an alternative method of similarity calculation, representing miRNAs and small molecules through continuous feature representation. This representation is learned by the proposed deep learning auto-encoder architecture. Our suggested representation was compared to previous works and achieved comparable results using 5-fold cross validation (92% identified within top 25% predictions), and better predictions for most of the case studies (avg. of 31% vs. 25% identified within the top 25% of predictions). The results proved the effectiveness of our proposed method to replace previous time- and effort-consuming methods.https://www.mdpi.com/1999-4923/14/1/3miRNA-small molecule associationsdrug repurposingdeep learning auto-encoderssequence encoding |
| spellingShingle | Ibrahim Abdelbaky Hilal Tayara Kil To Chong Identification of miRNA-Small Molecule Associations by Continuous Feature Representation Using Auto-Encoders Pharmaceutics miRNA-small molecule associations drug repurposing deep learning auto-encoders sequence encoding |
| title | Identification of miRNA-Small Molecule Associations by Continuous Feature Representation Using Auto-Encoders |
| title_full | Identification of miRNA-Small Molecule Associations by Continuous Feature Representation Using Auto-Encoders |
| title_fullStr | Identification of miRNA-Small Molecule Associations by Continuous Feature Representation Using Auto-Encoders |
| title_full_unstemmed | Identification of miRNA-Small Molecule Associations by Continuous Feature Representation Using Auto-Encoders |
| title_short | Identification of miRNA-Small Molecule Associations by Continuous Feature Representation Using Auto-Encoders |
| title_sort | identification of mirna small molecule associations by continuous feature representation using auto encoders |
| topic | miRNA-small molecule associations drug repurposing deep learning auto-encoders sequence encoding |
| url | https://www.mdpi.com/1999-4923/14/1/3 |
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