added paper mention

README.md

@@ -1,36 +1,37 @@
----
-title: OTRec
-app_file: app.py
-sdk: gradio
-sdk_version: 6.0.1
----
-# Disease–Target Recommender (Open Targets)
-
-This Space exposes a two-tower recommender model trained on Open Targets–derived
-disease–target data. Given a **disease ID** (matching the `diseaseId` column from
-the preprocessed data), it returns a ranked list of predicted **target IDs**.
-
-The backend is a TensorFlow / Keras model with:
-- A **query tower** for diseases (disease text + disease ID embedding)
-- A **key tower** for targets (target text only)
-- Cosine similarity between disease and target embeddings
-
-All candidate target embeddings are currently precomputed at startup for fast inference. (can drop)
-
----
-
-## Files and structure
-
-Expected repo layout:
-
-```text
-.
-├── app.py
-├── requirements.txt
-├── model.weights.h5
-└── data/
-    └── proc/
-        ├── disease_df.parquet
-        └── target_df.parquet
-        └── df_learn.parquet
-
+---
+title: OTRec
+app_file: app.py
+sdk: gradio
+sdk_version: 6.0.1
+---
+# Disease–Target Recommender (Open Targets)
+
+This Space exposes a two-tower recommender model trained on Open Targets–derived
+disease–target data. Given a **disease ID** (matching the `diseaseId` column from
+the preprocessed data), it returns a ranked list of predicted **target IDs**.
+
+The backend is a TensorFlow / Keras model with:
+- A **query tower** for diseases (disease text + disease ID embedding)
+- A **key tower** for targets (target text only)
+- Cosine similarity between disease and target embeddings
+
+All candidate target embeddings are currently precomputed at startup for fast inference. (can drop)
+
+This model is used for the paper "OTRec: prospective prediction of druggable target–disease associations via deep learning"
+---
+
+## Files and structure
+
+Expected repo layout:
+
+```text
+.
+├── app.py
+├── requirements.txt
+├── model.weights.h5
+└── data/
+    └── proc/
+        ├── disease_df.parquet
+        └── target_df.parquet
+        └── df_learn.parquet
+