Update src/about.py

src/about.py

@@ -38,18 +38,41 @@ LLM_BENCHMARKS_TEXT = f"""
     1. Semantic Similarity Inference:
       - This benchmark evaluates how well protein representation models can infer functional similarities between proteins. Ground truth functional similarities are derived from Gene Ontology (GO) annotations.
       - Different distance metrics (Cosine, Manhattan, Euclidean) are used to compute protein vector similarities, which are then correlated with the functional similarities.
+      
+      - Metrics (sim_ prefix):
+        • sim_sparse_MF_correlation/sim_200_MF_correlation/sim_500_MF_correlation: Correlation between protein embeddings and Molecular Function (MF) similarity scores
+        • sim_sparse_BP_correlation/sim_200_BP_correlation/sim_500_BP_correlation: Correlation between protein embeddings and Biological Process (BP) similarity scores
+        • sim_sparse_CC_correlation/sim_200_CC_correlation/sim_500_CC_correlation: Correlation between protein embeddings and Cellular Component (CC) similarity scores
+        • sim_sparse_Ave_correlation/sim_200_Ave_correlation/sim_500_Ave_correlation: Average correlation across MF, BP, and CC aspects
+        • sim_sparse_*_pvalue/sim_200_*_pvalue/sim_500_*_pvalue: Statistical significance (p-value) of the respective correlations
 
     2. Ontology-Based Protein Function Prediction (PFP):
-      - This benchmark assesses the ability of representation models to predict ontology-based functional annotations (GO terms). The models are tested on how well they classify proteins based on molecular functions, biological processes, and cellular components.
-      - A linear classifier is used to ensure that the models themselves are responsible for good performance, rather than the complexity of the classifier. 
+      - This benchmark assesses the ability of representation models to predict ontology-based functional annotations (GO terms). The models are tested on how well they classify proteins based on molecular functions (MF), biological processes (BP), and cellular components (CC).
+      - A linear classifier is used to ensure that the models themselves are responsible for good performance, rather than the complexity of the classifier.
+      
+      - Metrics (func_ prefix):
+        • func_BP_accuracy/func_CC_accuracy/func_MF_accuracy: Accuracy of predicting protein function in respective GO aspects
+        • func_BP_F1/func_CC_F1/func_MF_F1: F1 score for protein function prediction
+        • func_BP_precision/func_CC_precision/func_MF_precision: Precision of function prediction
+        • func_BP_recall/func_CC_recall/func_MF_recall: Recall of function prediction
+        • func_Ave_accuracy/func_Ave_F1/func_Ave_precision/func_Ave_recall: Average metrics across BP, CC, and MF aspects
 
     3. Drug Target Protein Family Classification:
       - This benchmark focuses on predicting the family of drug target proteins (enzymes, receptors, ion channels, etc.). This task tests the ability of models to learn structural features critical to these classifications.
       - The study evaluates models using datasets with varying sequence similarity thresholds (random, 50%, 30%, 15%) to ensure the models can predict beyond simple sequence similarity.
+      - Metrics (fam_ prefix):
+        • fam_nc_*_ave: Metrics for family classification on the non-clustered (nc) dataset
+        • fam_uc50_*_ave: Metrics for family classification on the UniRef50 clustered (uc50) dataset
+        • fam_uc30_*_ave: Metrics for family classification on the UniRef30 clustered (uc30) dataset
+        • fam_mm15_*_ave: Metrics for family classification on the maximum 15% sequence identity (mm15) dataset
 
     4. Protein–Protein Binding Affinity Estimation:
       - This benchmark evaluates models' ability to predict the change in binding affinities between proteins due to mutations. The dataset used is the **SKEMPI** dataset, which contains experimentally determined binding affinities.
       - The task measures how well models can extract critical structural features important for protein-protein interactions.
+      - Metrics (aff_ prefix):
+        • aff_mse_ave: Mean Squared Error for binding affinity prediction
+        • aff_mae_ave: Mean Absolute Error for binding affinity prediction
+        • aff_corr_ave: Correlation between predicted and actual binding affinity values
 
 ### PROBE is part of the the study entitled [Learning functional properties of proteins with language models](https://rdcu.be/cJAKN) which is schematically summarized in the figure below:<br/>
 """
@@ -84,7 +107,7 @@ Welcome to the PROBE (Protein RepresentatiOn BEnchmark) leaderboard! This platfo
 - **Protein Family**: Classifying drug target families.
 - **Protein Affinity**: Estimating binding affinities.
 
-Submit your own representation models and compare their performance across these tasks. For more details on how to participate, see the submission guidelines.
+Submit your own representation models and compare their performance across these tasks. For more details on how to participate, see the submission guidelines at Submit Here! tab. For descriptions of each benchmark and its metrics, please refer to the About tab.
 
 If you find PROBE useful, please consider citing our work: