Update src/about.py

src/about.py

@@ -38,6 +38,10 @@ 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.
+      - The benchmark uses three different datasets:
+        • Sparse: A sparse uniform dataset with broader protein coverage
+        • 200: A set of well-annotated 200 proteins
+        • 500: A set of well-annotated 500 proteins
       
       - 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
@@ -47,8 +51,9 @@ LLM_BENCHMARKS_TEXT = f"""
         • 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 (MF), biological processes (BP), and cellular components (CC).
+      - 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.
+      - The evaluation uses 5-fold cross-validation to ensure robust assessment.
       
       - Metrics (func_ prefix):
         • func_BP_accuracy/func_CC_accuracy/func_MF_accuracy: Accuracy of predicting protein function in respective GO aspects
@@ -59,7 +64,12 @@ LLM_BENCHMARKS_TEXT = f"""
 
     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.
+      - The benchmark uses datasets with varying sequence similarity thresholds to test generalization:
+        • nc: Non-clustered/Random split
+        • uc50: UniClust50 (50% sequence similarity-based split)
+        • uc30: UniClust30 (30% sequence similarity-based split)
+        • mm15: Maximum 15% sequence identity between train and test sets (MMseqs2-based split)
+      
       - 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
@@ -69,6 +79,8 @@ LLM_BENCHMARKS_TEXT = f"""
     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.
+      - Evaluation is performed through 10-fold cross-validation.
+      
       - Metrics (aff_ prefix):
         • aff_mse_ave: Mean Squared Error for binding affinity prediction
         • aff_mae_ave: Mean Absolute Error for binding affinity prediction