Update pages/2_About.py

pages/2_About.py

@@ -15,7 +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 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 = '[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.'
 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:'