Add predict button

pages/about.py

@@ -27,4 +27,10 @@ CIPHER is a knowledge graph-based AI algorithm for diagnostic and therapeutic di
 *Model training.* Next, to convert this trove of knowledge into an AI model with diagnostic and therapeutic capabilities, we employed graph representation learning, a deep learning method to model biomedical networks by embedding graphs into informative low-dimensional vector spaces. We trained a state-of-the-art heterogeneous graph Transformer to learn graph embeddings that encode the relationships in the KG.
             
 Through CIPHER, we seek to enable molecular subtyping and patient stratification of PD by integrating genetic and clinical progression data (*e.g.*, PPMI and HBS2.0 cohorts) and nominate genes, proteins, and pathways for in-depth mechanistic studies in stem cell and other PD models.
-""")
+""")
+
+col1, col2, col3 = st.columns(3)
+
+with col2:
+    if st.button("Predict with CIPHER"):
+        st.switch_page("pages/predict.py")

pages/predict.py

@@ -188,7 +188,6 @@ with st.spinner('Computing predictions...'):
         # Add legend
         ax.legend(loc = 'upper right', fontsize = 10)
         ax.grid(alpha = 0.2)
-        
 
         st.markdown(f"Out of 35,189 genes, the selected genes rank as follows:")
         selected_display_data['Rank'] = selected_display_data['Rank'].apply(lambda x: f"{x} (top {(100*x/target_nodes.shape[0]):.2f}% of predictions)")