Update README.md

README.md

@@ -1,3 +1,115 @@
----
-license: apache-2.0
----
+---
+license: apache-2.0
+tags:
+- bacformer
+- genomics
+- genome
+- bacteria
+- protein
+- phenotype
+- prokaryotes
+pretty_name: Bacformer genome embeddings with phenotypic traits labels dataset
+size_categories:
+- 10K<n<100K
+---
+
+# Dataset for predicting phenotypic traits labels using Bacformer embeddings
+
+A dataset containing Bacformer embeddings for a set of almost `25k` unique genomes downloaded from [NCBI GenBank](https://www.ncbi.nlm.nih.gov/genbank/)
+with associated phenotypic trait labels. 
+
+The phenotypic traits have been extracted from a number of sources [1, 2, 3] and include a diversity of categorical phenotypes. We exclude phenotypic 
+traits with a low nr of samples, giving us 139 uniqe phenotypic traits. 
+If the same or similar label appeared in two different sources, we kept it as separate labels as the label collection setup may differ for the labels.
+
+The Bacformer embeddings have been computed by running [macwiatrak/bacformer-masked-complete-genomes](https://huggingface.co/macwiatrak/bacformer-masked-complete-genomes/edit/main/README.md)
+in inference mode and averaging the contextual protein embeddings to get a genome embedding.
+
+For more info on how to embed genomes with Bacformer see github - https://github.com/macwiatrak/Bacformer.
+
+## Usage
+
+See the tutorial on predicting the phenotypic traits with Bacformer - [tutorial link](https://github.com/macwiatrak/Bacformer/blob/main/tutorials/phenotypic_traits_prediction_tutorial.ipynb).
+
+### Code snippet on how to train a linear regression model for a phenotype
+
+```python
+import numpy as np
+import pandas as pd
+from datasets import load_dataset
+
+from sklearn.model_selection import train_test_split
+from sklearn.metrics import roc_auc_score
+from sklearn.linear_model import LogisticRegression
+from sklearn.preprocessing import StandardScaler
+from sklearn.pipeline import Pipeline
+
+
+# load the dataset and convert to pandas DF
+df = load_dataset("macwiatrak/bacformer-genome-embeddings-with-phenotypic-traits-labels", split="train").to_pandas()
+
+# select the phenotype
+phenotype = "gideon_Catalase"
+# remove the genomes with NaN values for the phenotype of interest
+phenotype_df = df[df[phenotype].notna()].copy()
+
+
+# ------------------------------------------------------------------
+# 2  60 / 20 / 20 stratified split (train → 0.6, val → 0.2, test → 0.2)
+# ------------------------------------------------------------------
+X_train_val, X_test, y_train_val, y_test = train_test_split(
+    X, y, test_size=0.20, random_state=42, stratify=y
+)
+X_train, X_val, y_train, y_val = train_test_split(
+    X_train_val, y_train_val,
+    test_size=0.25,  # 0.25 × 0.80 = 0.20
+    random_state=42,
+    stratify=y_train_val
+)
+
+
+# ------------------------------------------------------------------
+# 3  Hyper-parameter search on validation set
+# ------------------------------------------------------------------
+param_grid = np.logspace(-4, 4, 9)      # 1e-4 … 1e4
+best_auc, best_C, best_model = -np.inf, None, None
+
+for C in param_grid:
+    model = Pipeline(
+        steps=[
+            ("scale", StandardScaler()),
+            ("clf", LogisticRegression(
+                C=C, solver="liblinear", max_iter=2000, penalty="l2"
+            ))
+        ]
+    )
+    model.fit(X_train, y_train)
+
+    val_probs = model.predict_proba(X_val)[:, 1]
+    auc = roc_auc_score(y_val, val_probs)
+
+    if auc > best_auc:
+        best_auc, best_C, best_model = auc, C, model
+
+print(f"Best C on validation: {best_C}  |  AUROC_val = {best_auc:.4f}")
+
+
+# ------------------------------------------------------------------
+# 4  Final evaluation on the held-out test set
+# ------------------------------------------------------------------
+test_probs = best_model.predict_proba(X_test)[:, 1]
+test_auc  = roc_auc_score(y_test, test_probs)
+
+print(f"AUROC_test = {test_auc:.4f}")
+```
+
+## Contact
+In case of questions/issues or feature requests please raise an issue on github - https://github.com/macwiatrak/Bacformer.
+
+## References
+
+[1] Madin, Joshua S., et al. "A synthesis of bacterial and archaeal phenotypic trait data." Scientific data 7.1 (2020): 170.
+
+[2] Weimann, Aaron, et al. "From genomes to phenotypes: Traitar, the microbial trait analyzer." MSystems 1.6 (2016): 10-1128.
+
+[3] Brbić, Maria, et al. "The landscape of microbial phenotypic traits and associated genes." Nucleic acids research (2016): gkw964.