Update About.py

pages/About.py

@@ -19,13 +19,13 @@ st.markdown(
 
 st.markdown(
     """
-This tool predicts functions of  queried proteins by propagating previously generated domain-function associations (Domain2GO mapping set). 
+Domain2GO predicts functions of queried proteins by propagating previously generated domain-function association predictions (Domain2GO mapping set). 
 
 Domain2GO is developed with the aim of identifying unknown protein functions by associating domains with Gene Ontology terms, thus defining the problem as domain function prediction. Domain2GO mappings are generated using the existing domain and GO annotation data. In order to obtain highly reliable associations, we employed statistical resampling and analyzed the co-occurrence patterns of domains and GO terms on the same proteins. 
 
 We applied Domain2GO to predict protein functions, by propagating domain-associated GO terms to proteins that are annotated with those domains. For protein function prediction performance evaluation and comparison against other methods, we employed CAFA3 challenge datasets. The results demonstrated the potential of Domain2GO, especially when predicting molecular function and biological process terms, as it performed better than baseline predictors and curated associations (Fmax = 0.48 and 0.36 for MFO and BPO, respectively).
 
-For more information on the construction of Domain2GO mappings, statistical analysis of mappings, and protein function prediction performance evaluation, please refer to our pre-print article:
+For more information on the construction of Domain2GO mappings, statistical analysis of mappings, calculation of probability scores and protein function prediction performance evaluation, please refer to our pre-print article:
 
 Ulusoy, E., & Dogan, T. (2022). Mutual Annotation-Based Prediction of Protein Domain Functions with Domain2GO. *bioRxiv*, 514980v1. [Link](https://www.biorxiv.org/content/10.1101/2022.11.03.514980v1)