Update README.md

README.md

@@ -1,10 +1,9 @@
 ---
 license: cc-by-4.0
 tags:
-- structural_biology
 - PPIs
 - mass_spectrometry
-- AlphaFold
+- biology
 pretty_name: >-
   DirectContacts2: A network of direct physical protein interactions derived
   from high throughput mass spectrometry experiments
@@ -13,7 +12,7 @@ repo: https://github.com/KDrewLab/DirectContacts2_analysis.git
 # DirectContacts2: A network of direct physical protein interactions derived from high throughput mass spectrometry experiments
 Proteins carry out cellular functions by self-assembling into functional complexes, a process that depends on direct physical interactions 
 between components. While tools like AlphaFold and RoseTTAFold have advanced structure prediction, they remain limited in scaling to the full 
-human proteome. DirectContacts2 addresses this challenge by integrating diverse large-scale datasets, including AP/MS (BioPlex1–3, Boldt et al., Hein et al.), 
+human proteome. DirectContacts2 addresses this challenge by integrating diverse large-scale protrin interaction datasets, including AP/MS (BioPlex1–3, Boldt et al., Hein et al.), 
 biochemical fractionation (Wan et al.), proximity labeling (Gupta et al., Youn et al.), and RNA pulldown (Treiber et al.), to predict whether ~26 million 
 human protein pairs interact directly or indirectly.
 
@@ -40,9 +39,67 @@ human protein pairs interact directly or indirectly.
     Reimand et al. g:Profiler-a web server for functional interpretation of gene lists (2016 update). Nucleic Acids Res. 2016 Jul 8;44(W1):W83-9. doi: 10.1093/nar/gkw199.
 
 ## Associated code 
-
+  Additional code examples can be found on our [GitHub](), including:
+  importing the [DirectContacts2]() model to make predictions, importing the 
+  training and testing data, or using the full feature matrix.
+  
 # Usage
 
 ## Accessing and using the model
 DirectContacts2 was constructed using [AutoGluon](https://auto.gluon.ai/stable/index.html) an auto-ML tool. The module [TabularPredictor](https://auto.gluon.ai/stable/api/autogluon.tabular.TabularPredictor.html)
-is used to is used train, test, and make predictions with the model.
+is used to is used train, test, and make predictions with the model.
+
+This can be downloaded using the following:
+
+      $ pip install autogluon==0.4.0
+
+Then it can be imported as:
+
+    >>> from autogluon.tabular import TabularPredictor
+Note that to perform operations with our model the **0.4.0 version** must be used
+
+The [DirectContacts2 model]() can be accessed through HuggingFace with [huggingface_hub](https://huggingface.co/docs/hub/index)
+
+     >>> from huggingface_hub import snapshot_download
+
+    >>> model_dir = snapshot_download(repo_id="sfisch/DirectContacts2_AutoGluon")
+
+    >>> predictor = TabularPredictor.load(f"{model_dir}/DirectContacts2_Autogluon_Model_20230405")
+
+## Using the training and testing data
+
+Both the train and test feature matrices can be loaded using the Huggingface [datasets](https://huggingface.co/docs/datasets/index) library.
+
+This can be done from the command-line using:
+    
+      $ pip install datasets
+
+When loading into Python use the following:
+
+    >>> from datasets import load_dataset
+    >>> dataset = load_dataset('sfisch/DirectContacts2')
+
+Training and test feature matrices can then be accessed as separate objects:
+
+    >>> train = dataset["train"].to_pandas()
+    >>> test = dataset["test"].to_pandas()
+
+Jupyter notebooks containing more in-depth examples of model training, testing, and generating predictions can be found on our [GitHub](https://github.com/KDrewLab/huMAP3.0_analysis/huMAP3.0_model_devel)
+
+## Accessing full feature matrix and all test/train interaction/complex files
+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/direct_contacts2_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")
+
+The other complex/interaction files can be downloaded in the same manner. The files within the 'reference_interactions' directory
+contain the complexes split from [Complex Portal](https://www.ebi.ac.uk/complexportal) into test and training sets. Within that directory you
+will also find the pairwise protein interactions that were used as positive and negative interactions for both the test and training sets.
+
+## Dataset card authors
+Samantha Fischer (sfisch6@uic.edu)