Update pages/2_About.py

pages/2_About.py

@@ -15,7 +15,7 @@ st.markdown("""
 </style>""", unsafe_allow_html=True)
 
 
-text = '[www.google.com]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.'
+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 +25,22 @@ text = 'Cankara, F., & Dogan, T. (2022). ASCARIS: Positional Feature Annotation
 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)
+
+
+
+text = '<p style="font-family:Trebuchet MS; font-size: 20px; font-weight:bold">Description of the Dimensions of ASCARIS SAV Representations</p>'
+st.markdown(f'<p class="title-text">{text}</p>', unsafe_allow_html=True)
+
+st.markdown(""" 
+ <style>
+  .main-text
+  { 
+  font-family:Trebuchet MS; font-size:14px;text-align: justify;font-weight:bold
+  }
+</style>""", unsafe_allow_html=True)
+
+
+text = "<p style="font-family:Trebuchet MS; font-size: 20px; font-weight:bold">In ASCARIS representations, dimensions 1-5 correspond to datapoint identifier, 6-9 correspond to physicochemical property values, 10-12 correspond to domain-related information, 13-14 correspond to information regarding variation's position on the protein (both the sasa value and the categorization), 15-44 correspond to binary correspondence between the variation and different types of positional annotations (1 dimension for each annotation type, for a total of 30 types), 45-74 correspond to spatial (Euclidian) distances between the variation and different types of positional annotations (1 dimension for each annotation type, for a total of 30 types).</p>"
+st.markdown(f'<p class="title-text">{text}</p>', unsafe_allow_html=True)
+
+