Key Features:

• Fine-tuned to compute semantic similarity between disease names.
• Achieves an F1 score of 0.88 in distinguishing protein-level interaction MTIs (functional MTIs, validated via western blot or reporter assay) and sequence-based predicted MTIs.
• Built for applications in understanding miRNA-gene regulatory networks, disease diagnosis, treatment, and drug discovery.

Full Model Architecture

SentenceTransformer(
  (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel 
  (1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
  (2): Normalize()
)

Usage (Sentence-Transformers)

pip install -U sentence-transformers

Then you can use the model like this:

# Load the pre-trained SBERT model
from sentence_transformers import SentenceTransformer, util

# Directly use the following code to download model from hugging face or Replace 'Baiming123/Calcu_Disease_Similarity' with the local path to run model
model = SentenceTransformer("Baiming123/Calcu_Disease_Similarity")

# Example usage
disease1 = "lung cancer"
disease2 = "pulmonary fibrosis"

def sts(sentence_a, sentence_b) -> float:

  query_emb = model.encode(sentence_a)
  doc_emb = model.encode(sentence_b)
  [score] = util.dot_score(query_emb, doc_emb)[0].tolist()

  return score

similarity = sts(disease1, disease2)
print(similarity)

Additional Information

License

This model is licensed under CC-BY-NC 4.0 International license. If you use this model, please adhere to the license requirements.

Questions or Issues

If you encounter any issues or have any questions while using the model, feel free to reach out to the author for assistance. Thank you for your support and for using this model!