diff --git a/.gitignore b/.gitignore
index f900589f8..cc1b124e7 100644
--- a/.gitignore
+++ b/.gitignore
@@ -5,6 +5,7 @@
 resources/
 resources_test/
 output/
+out/
 target/
 local/
 datasets_raw/
diff --git a/README.md b/README.md
index c69886322..89c44ab1d 100644
--- a/README.md
+++ b/README.md
@@ -41,7 +41,7 @@ Similarly, run the command for other methods.
 ## Evaluate a GRN
 
 ``` bash
-scripts/benchmark_grn.sh --grn resources/grn-benchmark/models/collectri.csv 
+scripts/run_evaluation.sh --grn resources/grn-benchmark/grn_models/collectri.csv 
 ```
 
 Similarly, run the command for other GRN models.
@@ -100,10 +100,11 @@ approaches to assess both accuracy and comprehensiveness.
 
 ## Authors & contributors
 
-| name              | roles  |
-|:------------------|:-------|
-| Jalil Nourisa     | author |
-| Robrecht Cannoodt | author |
+| name               | roles  |
+|:-------------------|:-------|
+| Jalil Nourisa      | author |
+| Robrecht Cannoodt  | author |
+| Antoine Passemiers | author |
 
 ## API
 
diff --git a/scripts/run_evaluation.sh b/scripts/run_evaluation.sh
index 7b1a16cf9..bb93950ca 100644
--- a/scripts/run_evaluation.sh
+++ b/scripts/run_evaluation.sh
@@ -27,10 +27,21 @@ fi
 # Print parsed arguments (for debugging purposes)
 echo "GRN file: $grn"
 echo "Sample value: $sample"
-echo "Sample value: $reg_type"
+echo "Regression model: $reg_type"
 
-# Example operation using the arguments
+# Clean bin/ folder
+rm -r bin
+mkdir bin
+
+# Run regression analysis 1
 echo "Running GRN benchmark with $grn and sample size $sample"
 echo "Regression 1"
-viash build src/metrics/regression_1/config.vsh.yaml -p docker -o bin 
-bin/regression_1 --perturbation_data resources/grn-benchmark/perturbation_data.h5ad --reg_type $reg_type --prediction $grn --score $score
+mkdir -p bin/regression_1
+viash build src/metrics/regression_1/config.vsh.yaml -p docker -o bin/regression_1
+bin/regression_1/regression_1 --perturbation_data resources/grn-benchmark/perturbation_data.h5ad --reg_type $reg_type --prediction $grn --score $score
+
+# Run regression analysis 2
+echo "Regression 2"
+mkdir -p bin/regression_2
+viash build src/metrics/regression_2/config.vsh.yaml -p docker -o bin/regression_2
+bin/regression_2/regression_2 --perturbation_data resources/grn-benchmark/perturbation_data.h5ad --reg_type $reg_type --prediction $grn --score $score
diff --git a/src/metrics/regression_2/config.vsh.yaml b/src/metrics/regression_2/config.vsh.yaml
new file mode 100644
index 000000000..8fd828db5
--- /dev/null
+++ b/src/metrics/regression_2/config.vsh.yaml
@@ -0,0 +1,21 @@
+__merge__: ../../api/comp_metric.yaml
+functionality:
+  name: regression_2
+  info:
+    label: Regression 2
+    summary: Calculates R2 score for regression 2
+    description: |
+      Calculates R2 score using regression approach 2.
+  resources:
+    - type: python_script
+      path: script.py
+    - path: main.py
+platforms:
+  - type: docker
+    image: ghcr.io/openproblems-bio/base_python:1.0.4
+    setup:
+      - type: python
+        packages: [ lightgbm ]
+  - type: nextflow
+    directives:
+      label: [ midtime, highmem, highcpu ]
\ No newline at end of file
diff --git a/src/metrics/regression_2/main.py b/src/metrics/regression_2/main.py
new file mode 100644
index 000000000..0fb282e11
--- /dev/null
+++ b/src/metrics/regression_2/main.py
@@ -0,0 +1,221 @@
+from typing import Dict, List, Tuple, Any, Union
+import random
+
+import tqdm
+import numpy as np
+import lightgbm
+import pandas as pd
+import anndata as ad
+from sklearn.preprocessing import LabelEncoder, RobustScaler, StandardScaler
+from sklearn.model_selection import GroupKFold, LeaveOneGroupOut
+from sklearn.linear_model import Ridge
+from sklearn.metrics import r2_score, mean_squared_error
+
+
+SEED = 0xCAFE
+N_POINTS_TO_ESTIMATE_BACKGROUND = 20
+
+
+def load_grn(filepath: str, gene_names: np.ndarray) -> np.ndarray:
+    gene_dict = {gene_name: i for i, gene_name in enumerate(gene_names)}
+    A = np.zeros((len(gene_names), len(gene_names)), dtype=float)
+    df = pd.read_csv(filepath, sep=',', header='infer', index_col=0)
+    for source, target, weight in zip(df['source'], df['target'], df['weight']):
+        if (source not in gene_dict) or (target not in gene_dict):
+            continue
+        i = gene_dict[source]
+        j = gene_dict[target]
+        A[i, j] = float(weight)
+    return A
+
+
+def fill_zeros_in_grn(A: np.ndarray, eps: float = 1e-10) -> np.ndarray:
+    A = np.copy(A)
+    A[A > 0] = A[A > 0] + eps
+    A[A < 0] = A[A < 0] - eps
+    A[A == 0] = np.random.rand(*A[A == 0].shape) * 2 * eps - eps
+    A += np.random.rand(*A.shape) * eps
+    return A
+
+
+def cross_validate_gene(
+        estimator_t: str,
+        X: np.ndarray,
+        groups: np.ndarray,
+        grn: np.ndarray,
+        j: int,
+        n_features: int = 10,
+        random_state: int = 0xCAFE
+) -> Dict[str, float]:
+    
+    results = {'r2': np.nan, 'mse': np.nan, 'avg-r2': np.nan}
+    
+    if n_features == 0:
+        return results
+    
+    # Feature selection
+    scores = np.abs(grn[:, j])
+    scores[j] = -1
+    selected_features = np.argsort(scores)[-n_features:]
+    assert j not in selected_features
+    X_ = X[:, selected_features]
+
+    # Define labels
+    y_ = X[:, j]
+
+    y_pred, y_target, r2s = [], [], []
+    for t, (train_index, test_index) in enumerate(LeaveOneGroupOut().split(X_, y_, groups)):
+
+        if estimator_t == 'ridge':
+            model = Ridge(random_state=random_state)
+        elif estimator_t == 'GB':
+            model = lightgbm.LGBMRegressor(verbosity=-1, n_estimators=100, n_jobs=4, random_state=random_state)
+        else:
+            raise NotImplementedError(f'Unknown model "{estimator_t}"')
+
+        X_train = X_[train_index, :]
+        X_test = X_[test_index, :]
+        y_train = y_[train_index]
+        y_test = y_[test_index]
+        
+        model.fit(X_train, y_train)
+
+        y_pred.append(model.predict(X_test))
+        y_target.append(y_test)
+        r2s.append(r2_score(y_target[-1], y_pred[-1]))
+
+    y_pred = np.concatenate(y_pred, axis=0)
+    y_target = np.concatenate(y_target, axis=0)
+    
+    results['r2'] = float(r2_score(y_target, y_pred))
+    results['mse'] = float(mean_squared_error(y_target, y_pred))
+    results['avg-r2'] = float(np.mean(r2s))
+    
+    return results
+
+
+def learn_background_distribution(
+        estimator_t: str,
+        X: np.ndarray,
+        groups: np.ndarray,
+        max_n_regulators: int,
+        random_state: int = 0xCAFE
+) -> Dict[int, Tuple[float, float]]:
+
+    rng = np.random.default_rng(seed=random_state)
+
+    n_genes = X.shape[1]
+    random_grn = rng.random(size=(n_genes, n_genes))
+
+    background = {}
+    for n_features in tqdm.tqdm(range(1, max_n_regulators + 1), desc='Estimating background dist'):
+        scores = []
+        for _ in range(N_POINTS_TO_ESTIMATE_BACKGROUND):
+            j = rng.integers(low=0, high=n_genes)
+            random_grn[:, j] = rng.random(size=n_genes)
+            res = cross_validate_gene(
+                estimator_t,
+                X,
+                groups,
+                random_grn,
+                j,
+                n_features=n_features,
+                random_state=random_state
+            )
+            scores.append(res['avg-r2'])
+        background[n_features] = (np.mean(scores), max(0.001, np.std(scores)))
+    background['max'] = background[max_n_regulators]
+    return background
+
+
+def cross_validate(
+        estimator_t: str,
+        gene_names: np.ndarray,
+        X: np.ndarray,
+        groups: np.ndarray,
+        grn: np.ndarray,
+        n_features: np.ndarray
+) -> List[Dict[str, float]]:
+    n_genes = len(grn)
+    
+    grn = fill_zeros_in_grn(grn)
+    
+    results = []
+    for j in tqdm.tqdm(range(n_genes), desc=f'{estimator_t} CV'):
+        res = cross_validate_gene(estimator_t, X, groups, grn, j, n_features=int(n_features[j]))
+        results.append(res)
+    
+    return {
+        'gene_names': list(gene_names),
+        'scores': list(results)
+    }
+
+
+def main(par: Dict[str, Any]) -> pd.DataFrame:
+
+    # Set global seed for reproducibility purposes
+    random_state = SEED
+    np.random.seed(random_state)
+    random.seed(random_state)
+    lightgbm.LGBMRegressor().set_params(random_state=random_state)
+
+    print('Reading input files', flush=True)
+    
+    # Load perturbation data
+    perturbation_data = ad.read_h5ad(par['perturbation_data'])
+    gene_names = perturbation_data.var.index.to_numpy()
+    n_genes = len(gene_names)
+    groups = LabelEncoder().fit_transform(perturbation_data.obs.plate_name)
+    
+    # Load inferred GRN
+    grn = load_grn(par['prediction'], gene_names)
+    
+    results = []
+    layers = ['scgen_pearson', 'scgen_lognorm']
+    for layer in layers:
+        print(f'Compute metrics for layer: {layer}', flush=True)
+
+        # Load and standardize perturbation data
+        X = perturbation_data.layers[layer]
+        X = RobustScaler().fit_transform(X)
+
+        # Determine maximum number of input features
+        max_n_regulators = min(100, int(0.5 * n_genes))
+
+        # Learn background distribution for each value of `n_features`:
+        # r2 scores using random genes as features.
+        background = learn_background_distribution(par['reg_type'], X, groups, max_n_regulators, random_state=random_state)
+
+        # Cross-validate each gene using the inferred GRN to define select input features
+        res = cross_validate(
+            par['reg_type'],
+            gene_names,
+            X,
+            groups,
+            grn,
+            np.clip(np.sum(grn != 0, axis=0), 0, max_n_regulators)
+        )
+
+        # Compute z-scores from r2 scores to take into account the fact
+        # that random network can still perform well when the number of features is large
+        scores = []
+        for j in range(n_genes):
+            if np.isnan(res['scores'][j]['avg-r2']):
+                continue
+            n_features = int(np.sum(grn[:, j] != 0))
+            if n_features in background:
+                mu, sigma = background[n_features]
+            else:
+                mu, sigma = background['max']
+            z_score = (res['scores'][j]['avg-r2'] - mu) / sigma
+            z_score = max(0, z_score)
+            scores.append(z_score)
+
+        total_score = np.mean(scores)
+        print(f'Score on {layer}: {total_score}')
+        results.append({'score': total_score})
+
+    # Convert results to DataFrame
+    df_results = pd.DataFrame(results, index=layers)
+    
+    return df_results
diff --git a/src/metrics/regression_2/script.py b/src/metrics/regression_2/script.py
new file mode 100644
index 000000000..3f0822daf
--- /dev/null
+++ b/src/metrics/regression_2/script.py
@@ -0,0 +1,29 @@
+import pandas as pd
+import anndata as ad
+import sys
+import numpy as np
+
+## VIASH START
+par = {
+  'perturbation_data': 'resources/grn-benchmark/perturbation_data.h5ad',
+  'prediction': 'resources/grn-benchmark/negative_control.csv',
+  'score': 'output/score.csv',
+  'reg_type': 'ridge'
+}
+
+## VIASH END
+print('Reading input data')
+
+sys.path.append(meta['resources_dir'])
+from main import main 
+
+output = main(par) 
+output = output.mean(axis=0).to_frame().T # average across datasets
+print(output)
+output.columns = [f'S{i + 1}' for i in range(len(output.columns))]
+output['Overall'] = output.mean(axis=1)
+
+print('Write output to file', flush=True)
+print(output)
+output.to_csv(par['score'])
+print('Completed', flush=True)