AEmiGAP: AutoEncoder-Based miRNA–Gene Association Prediction Using Deep Learning Method
Seungwon Yoon, Hyewon Yoon, Jaeeun Cho, Kyuchul Lee · 2024
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Description
Seungwon Yoon and co-authors present AEmiGAP, a deep learning model that predicts which genes tiny regulatory RNAs target, in a 2024 paper. These microRNAs steer gene activity and link to many diseases, cancer among them. The model pairs an autoencoder, which compresses data into rich features, with a memory network that reads patterns over time.
The team built a cleaned set of known and non-target pairs using distance-based filtering, which sharpened training. AEmiGAP beat all earlier models on the standard area score and overall accuracy. Two examples follow: the thirty highest-scored new pairs, and the ten microRNAs tied most strongly to each of five key cancer genes. The authors offer the model as a guide for lab follow-up in cancer research.
Catalog record
| CREATOR | Seungwon Yoon, Hyewon Yoon, Jaeeun Cho, Kyuchul Lee |
|---|---|
| YEAR | 2024 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 72 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |