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سی و سومین کنفرانس بین المللی مهندسی برق
A Siamese Neural Network for Predicting snoRNA-Disease Association
نویسندگان :
Milad Besharatifard
1
Fatemeh Zare-Mirakabad
2
1- Amirkabir University of Technology (Tehran Polytechnic)
2- Amirkabir University of Technology (Tehran Polytechnic)
کلمات کلیدی :
snoRNA،Disease،Siamese Neural Network،Machine Learning
چکیده :
Small nucleolar RNAs (snoRNAs) are essential in biological processes and are linked to diseases such as cancer and neurodegenerative disorders. Identifying snoRNA-disease associations is challenging due to limited and imbalanced data. To address this, we propose a Siamese Neural Network (SNN) framework that utilizes features derived from 4-mer sequence patterns and DNABERT for snoRNAs, along with binary profiles of diseases based on semantic relationships and phenotype associations. The SNN processes these features through parallel subnetworks, generating embeddings in a shared space to predict associations. Trained with binary cross-entropy and Kullback-Leibler divergence losses, our model achieves superior performance compared to existing methods, with an AUC of 0.91, AUPR of 0.57, and accuracy of 0.91. These results demonstrate the effectiveness of our approach in identifying meaningful snoRNA-disease relationships, offering a scalable and efficient tool for biomarker discovery and therapeutic research.
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