Update README.md

Added additional file descriptions and references for the test feature matrix with intra- and inter-complex negatives.

README.md

@@ -85,13 +85,19 @@ human protein pairs interact directly or indirectly.
   
     |-- test_FeatureMatrix_pdbsize5_only_INTRA_complex_NegativePairs_20240326.csv.gz
     
-      This is the feature matrix for the test set of interactions.
+      This is the feature matrix for the test set of interactions. This feature matrix only contains
+      intra-complex negative protein pairs.
       
   |-- **train**
   
     |-- train_FeatureMatrix_pdbsize3_20240326.csv.gz
     
       This is the feature matrix for the training set of interactions.
+
+  |-- **alternative_test_feature_matrix**
+    |-- DirectContacts2_test_feature_matrix_inter_and_intra_negatives_20260624.csv.gz
+
+      This is the feature matrix for the test set that contains intra- and inter-complex negative protein pairs.
       
 ## Associated code 
   Additional code examples can be found on our [GitHub](https://github.com/KDrewLab/DirectContacts2_analysis.git), including:
@@ -119,7 +125,7 @@ The [DirectContacts2 model](https://huggingface.co/DrewLab/DirectContacts2_AutoG
 
      >>> from huggingface_hub import snapshot_download
 
-    >>> model_dir = snapshot_download(repo_id="sfisch/DirectContacts2_AutoGluon")
+    >>> model_dir = snapshot_download(repo_id="DrewLab/DirectContacts2_AutoGluon")
 
     >>> predictor = TabularPredictor.load(f"{model_dir}/DirectContacts2_Autogluon_Model")
 
@@ -134,7 +140,7 @@ This can be done from the command-line using:
 When loading into Python use the following:
 
     >>> from datasets import load_dataset
-    >>> dataset = load_dataset('sfisch/DirectContacts2')
+    >>> dataset = load_dataset('DrewLab/DirectContacts2')
 
 Training and test feature matrices can then be accessed as separate objects:
 
@@ -143,18 +149,16 @@ Training and test feature matrices can then be accessed as separate objects:
 
 Jupyter notebooks containing more in-depth examples of model training, testing, and generating predictions can be found on our [GitHub](https://github.com/KDrewLab/DirectContacts2_analysis/tree/main)
 
-## Accessing full feature matrix and all test/train interaction/complex files
+## Accessing full feature matrix, all test/train interaction/complex files, and the alternative test feature matrix
 All other files, such as the full feature matrix, can be accessed via Huggingface_hub.
 
     >>> from huggingface_hub import hf_hub_download
-    >>> full_file = hf_hub_download(repo_id="sfisch/DirectContacts2", filename='full/humap3_full_feature_matrix_20220625.csv.gz', repo_type='dataset')
+    >>> full_file = hf_hub_download(repo_id="DrewLab/DirectContacts2", filename='full/humap3_full_feature_matrix_20220625.csv.gz', repo_type='dataset')
 
 This just provides the file for download. Depending on your workflow, if you wish to use as a pandas dataframe for example:
 
     >>> import pandas as pd
     >>> full_featmat = pd.read_csv(full_file, compression="gzip")
 
-
-
 ## Dataset card authors
 Samantha Fischer (sfisch6@uic.edu)