Update README.md

README.md

@@ -5,4 +5,53 @@ metrics:
 - f1
 base_model:
 - facebook/esm1b_t33_650M_UR50S
----
+---
+
+## **Fine-Tuning ESM-1b for Phosphosite Prediction**
+
+This repository provides a fine-tuned version of the [ESM-1b]([https://website-name.com](https://huggingface.co/facebook/esm1b_t33_650M_UR50S)) model, trained to classify phosphosites using unlabeled phosphosites(ie, which kinases phosphorylate those phosphosites is unknown) from [PhosphoSitePlus](https://www.phosphosite.org/staticDownloads). The model is designed for binary classification, distinguishing phosphosites from non-phosphorylated peptid sequences [(Musite, a Tool for Global Prediction of General and Kinase-specific Phosphorylation Sites)](https://www.sciencedirect.com/science/article/pii/S1535947620311518)
+
+### **Dataset & Labeling Strategy**
+
+The dataset was constructed using phosphosite information from **PhosphoSitePlus**, with the following assumptions:
+
+- Positive Samples: Known phosphorylated residues from PhosphoSitePlus.
+- Negative Samples: Derived by selecting 15-residue sequences from the same proteins, ensuring the central residue matches a known phosphorylation site but is not reported as phosphorylated in PhosphoSitePlus.
+**Note**: The absence of phosphorylation reports does not imply absolute non-phosphorylation but is assumed as negative in this study.
+
+### **Dataset Statistics**
+- Positive Samples: 366,028
+- Negative Samples: 364,121
+- Training Samples: 511,104
+- Validation Samples: 109,522
+- Testing Samples: 109,523
+
+### **Test Performance**
+- Accuracy: 0.94
+- F1-Score: 0.94
+
+### **Usage**
+```
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import torch
+
+# Load the model and tokenizer
+model_name = "isikz/phosphorylation_binaryclassification_esm1b"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForSequenceClassification.from_pretrained(model_name)
+
+# Example sequence
+sequence = "MKTLLLTLVVVTIVCLDLGYTGV"
+
+# Tokenize input
+inputs = tokenizer(sequence, return_tensors="pt")
+
+# Get prediction
+with torch.no_grad():
+outputs = model(**inputs)
+logits = outputs.logits
+prediction = torch.sigmoid(logits).item()
+
+print(f"Phosphorylation Probability: {prediction:.4f}")
+
+```