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README.md
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# Model Description
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This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search.The 'Calcu_Disease_Similarity' model is designed to encode two disease terms and compute their **semantic similarity**. The model has been fine-tuned on disease-related datasets 'MeSHDS' and achieves a high F1 score in distinguishing experimentally validated miRNA-target interactions (MTIs) and predicted MTIs by considering disease similarity.
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eprint = {https://www.biorxiv.org/content/early/2025/02/16/2024.05.17.594604.full.pdf},
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journal = {bioRxiv}
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}
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```
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## Key Features:
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- Fine-tuned to compute semantic similarity between disease names.
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<!-- # Model Description
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This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search.The 'Calcu_Disease_Similarity' model is designed to encode two disease terms and compute their **semantic similarity**. The model has been fine-tuned on disease-related datasets 'MeSHDS' and achieves a high F1 score in distinguishing experimentally validated miRNA-target interactions (MTIs) and predicted MTIs by considering disease similarity.
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eprint = {https://www.biorxiv.org/content/early/2025/02/16/2024.05.17.594604.full.pdf},
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journal = {bioRxiv}
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}
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``` -->
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## Key Features:
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- Fine-tuned to compute semantic similarity between disease names.
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