Rename pages/About.py to pages/1_About.py

pages/{About.py → 1_About.py}

@@ -1,9 +1,10 @@
 import streamlit as st
 
-original_title = '<p style="font-family:Trebuchet MS; color:#FD7456; font-size: 35px; font-weight:bold">ASCARIS:  Positional Feature Annotation and Protein Structure-Based Representation of Single Amino Acid Variations</p>'
+original_title = '<p style="font-family:Trebuchet MS; color:#FD7456; font-size: 25px; font-weight:bold">ASCARIS:  Positional Feature Annotation and Protein Structure-Based Representation of Single Amino Acid Variations</p>'
 st.markdown(original_title, unsafe_allow_html=True)
 
-
+text = '<p style="font-family:Trebuchet MS; font-size: 20px; font-weight:bold">Developers: Fatma Cankara & Tunca Dogan</p>'
+st.markdown(f'<p class="title-text">{text}</p>', unsafe_allow_html=True)
 
 st.markdown(""" 
  <style>
@@ -14,7 +15,7 @@ st.markdown("""
 </style>""", unsafe_allow_html=True)
 
 
-text = 'ASCARIS (Annotation and StruCture-bAsed RepresentatIon of Single amino acid variations) is a tool for the featurization (i.e., quantitative representation) of SAVs, which could be used for a variety of purposes, such as predicting their functional effects or building multi-omics-based integrative models. ASCARIS utilizes the correspondence between the location of the SAV on the sequence and 30 different types of positional feature annotations (e.g., active/lipidation/glycosylation sites; calcium/metal/DNA binding, inter/transmembrane regions, etc.) from UniProt, along with structural features and the change in physicochemical properties, using models from PDB and AlphaFold-DB. It constructs a 74-dimensional feature set (including meta-data) to represent a given SAV.'
+text = 'ASCARIS (Annotation and StruCture-bAsed RepresentatIon of Single amino acid variations) is a tool for the featurization (i.e., quantitative representation) of single amino acid variations (SAVs), which could be used for a variety of purposes, such as predicting their functional effects or building multi-omics-based integrative models. ASCARIS utilizes the correspondence between the location of the SAV on the sequence and 30 different types of positional feature annotations (e.g., active/lipidation/glycosylation sites; calcium/metal/DNA binding, inter/transmembrane regions, etc.) from UniProt, along with structural features and the change in physicochemical properties, using models from PDB and AlphaFold-DB. It constructs a 74-dimensional feature set (including meta-data) to represent a given SAV.'
 st.markdown(f'<p class="title-text">{text}</p>', unsafe_allow_html=True)
 
 text = 'Please refer to our pre-print article for more information on the construction of feature vectors, statistical analysis of features, and machine learning models trained on ASCARIS representations to predict the effect of SAVs:'
@@ -25,5 +26,4 @@ st.markdown(f'<p class="title-text">{text}</p>', unsafe_allow_html=True)
 
 st.image('visuals/concept_figure.png')
 text = 'ASCARIS Work Scheme'
-st.markdown(f'<p style="text-align:center">{text}</p>', unsafe_allow_html=True)
-
+st.markdown(f'<p style="text-align:center">{text}</p>', unsafe_allow_html=True)