Update README.md

README.md

@@ -135,13 +135,15 @@ aging clocks:
 `pip install computage`
 
 Now, suppose you have trained your brand-new epigenetic aging clock model using the classic `scikit-learn` library. You saved your model as `pickle` file. 
-Then, the following block of code can be used to benchmark your model. We also added several published aging clock models for comparison.
+Then, the following block of code can be used to benchmark your model. 
+We also implemented imputation of missing values from the [SeSAMe package](https://github.com/zwdzwd/sesame) 
+and added several published aging clock models for comparison. 
 
 ```python
 from computage import run_benchmark
 
 # first, define a method to impute NaNs for the in_library models
-# we recommend using imputation with gold standard values from the [SeSAMe package](https://github.com/zwdzwd/sesame)
+# we recommend using imputation with gold standard values from SeSAMe
 imputation = 'sesame_450k'
 
 # for example, take these three clock models for benchmarking
@@ -169,7 +171,7 @@ bench = run_benchmark(models_config,
 
 ### Explore the dataset
 
-In case you want just to explore our dataset locally, use the following commands for downloading:
+In case you only want to explore our dataset locally, use the following commands to download it:
 
 ```python
 from huggingface_hub import snapshot_download
@@ -178,9 +180,7 @@ snapshot_download(
     repo_type="dataset",
     local_dir='.')
 ```
-
 Once downloaded, the dataset can be open with `pandas` (or any other `parquet` reader).
-
 ```python
 import pandas as pd