pages/about.py

import streamlit as st
from menu import menu_with_redirect

# Path manipulation
from pathlib import Path

# Custom and other imports
import project_config

# Redirect to app.py if not logged in, otherwise show the navigation menu
menu_with_redirect()

# Header
st.image(str(project_config.MEDIA_DIR / 'about_header.svg'), use_column_width=True)

# Main content
st.markdown("Welcome to CIPHER, a knowledge-grounded artificial intelligence (AI) system for **C**ontextually **I**nformed **P**recision **HE**althca**R**e in Parkinson's disease (PD).")

# Subheader
st.subheader("About CIPHER", divider = "grey")

st.markdown("""
CIPHER is a knowledge graph-based AI algorithm for diagnostic and therapeutic discovery in PD.
            
*Knowledge graph construction.* To create CIPHER, we integrated diverse public information about basic biomedical interactions into a harmonized data platform amenable for training large-scale AI models. Specifically, we constructed a multiscale heterogeneous knowledge graph (KG) with *n* = 143,093 nodes and *n* = 7,048,795 edges by curating 36 high-quality primary data sources, ontologies, and knowledge bases.
            
*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")