diff --git a/biolearn/data/MiAge.csv b/biolearn/data/MiAge.csv
index cea4762..8c84485 100644
--- a/biolearn/data/MiAge.csv
+++ b/biolearn/data/MiAge.csv
@@ -1,269 +1,269 @@
-CpGmarker,CoefficientTraining
-cg11823511,Age-hyper
-cg12212311,Age-hyper
-cg17760405,Age-hyper
-cg17757602,Age-hyper
-cg05937737,Age-hyper
-cg01923218,Age-hyper
-cg21264189,Age-hyper
-cg09053680,Age-hyper
-cg22802813,Age-hyper
-cg21334513,Age-hyper
-cg17034390,Age-hyper
-cg07783282,Age-hyper
-cg08445802,Age-hyper
-cg02874376,Age-hyper
-cg24708471,Age-hyper
-cg25285090,Age-hyper
-cg17265829,Age-hyper
-cg27282900,Age-hyper
-cg14192957,Age-hyper
-cg03409187,Age-hyper
-cg23189410,Age-hyper
-cg17794299,Age-hyper
-cg26267854,Age-hyper
-cg11205072,Age-hyper
-cg05372113,Age-hyper
-cg17694795,Age-hyper
-cg11149930,Age-hyper
-cg18488157,Age-hyper
-cg17078116,Age-hyper
-cg00437985,Age-hyper
-cg13954457,Age-hyper
-cg05745631,Age-hyper
-cg08701621,Age-hyper
-cg06794543,Age-hyper
-cg06607764,Age-hyper
-cg19841369,Age-hyper
-cg06454760,Age-hyper
-cg15909981,Age-hyper
-cg13201808,Age-hyper
-cg08847173,Age-hyper
-cg00370229,Age-hyper
-cg04007726,Age-hyper
-cg04118306,Age-hyper
-cg21136104,Age-hyper
-cg17812788,Age-hyper
-cg18687675,Age-hyper
-cg14627172,Age-hyper
-cg14822490,Age-hyper
-cg12188986,Age-hyper
-cg05525743,Age-hyper
-cg09907936,Age-hyper
-cg07766263,Age-hyper
-cg21730858,Age-hyper
-cg12492087,Age-hyper
-cg02895639,Age-hyper
-cg26709285,Age-hyper
-cg00689492,Age-hyper
-cg15490715,Age-hyper
-cg25537993,Age-hyper
-cg04599403,Age-hyper
-cg19761848,Age-hyper
-cg22727783,Age-hyper
-cg13879776,Age-hyper
-cg19947104,Age-hyper
-cg00303982,Age-hyper
-cg04484550,Age-hyper
-cg19971388,Age-hyper
-cg11324957,Age-hyper
-cg00298065,Age-hyper
-cg04191427,Age-hyper
-cg04958794,Age-hyper
-cg04293085,Age-hyper
-cg02049405,Age-hyper
-cg17434309,Age-hyper
-cg05068206,Age-hyper
-cg26890189,Age-hyper
-cg05206884,Age-hyper
-cg14182974,Age-hyper
-cg09660365,Age-hyper
-cg03192598,Age-hyper
-cg04017533,Age-hyper
-cg25247859,Age-hyper
-cg24368848,Age-hyper
-cg06708634,Age-hyper
-cg05080154,Age-hyper
-cg12444411,Age-hyper
-cg12353688,Age-hyper
-cg12215340,Age-hyper
-cg00465970,Age-hyper
-cg21143896,Age-hyper
-cg12046758,Age-hyper
-cg09354556,Age-hyper
-cg18430128,Age-hyper
-cg08843809,Age-hyper
-cg18146737,Age-hyper
-cg09224821,Age-hyper
-cg12250896,Age-hyper
-cg14013195,Age-hyper
-cg14600824,Age-hyper
-cg25975712,Age-hyper
-cg11962640,Age-hyper
-cg12406391,Age-hyper
-cg08390209,Age-hyper
-cg18628732,Age-hyper
-cg12017745,Age-hyper
-cg12399536,Age-hyper
-cg22499720,Age-hyper
-cg27088072,Age-hyper
-cg07105285,Age-hyper
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-cg05962092,Age-hyper
-cg13434842,Age-hyper
-cg02578944,Age-hyper
-cg08584759,Age-hyper
-cg11052143,Age-hyper
-cg22319311,Age-hyper
-cg13405332,Age-hyper
-cg04716021,Age-hyper
-cg13687570,Age-hyper
-cg26612727,Age-hyper
-cg15691862,Age-hyper
-cg26557693,Age-hyper
-cg06654118,Age-hyper
-cg13933080,Age-hyper
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-cg22320365,Age-hyper
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-cg05212464,Age-hyper
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-cg11599981,Age-hyper
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-cg00510437,Age-hyper
-cg04723343,Age-hyper
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-cg19341977,Age-hyper
-cg02793099,Age-hyper
-cg02773588,Age-hyper
-cg10204755,Age-hyper
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-cg17250863,Age-hyper
-cg17676129,Age-hyper
-cg22294267,Age-hyper
-cg17029193,Age-hyper
-cg08171483,Age-hyper
-cg19336198,Age-hyper
-cg23830290,Age-hyper
-cg03984347,Age-hyper
-cg12143651,Age-hyper
-cg16624069,Age-hyper
-cg09506661,Age-hyper
-cg06085683,Age-hyper
-cg07004744,Age-hyper
-cg07957995,Age-hyper
-cg07450698,Age-hyper
-cg05395187,Age-hyper
-cg14184780,Age-hyper
-cg15777825,Age-hyper
-cg13126279,Age-hyper
+CpG,b,c,d
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diff --git a/biolearn/model.py b/biolearn/model.py
index 4109ebf..aeca651 100644
--- a/biolearn/model.py
+++ b/biolearn/model.py
@@ -5,6 +5,7 @@
 import torch
 import torch.nn as nn
 import torch.nn.functional as F
+from scipy.optimize import minimize_scalar
 from sklearn.linear_model import LinearRegression
 
 from biolearn.data_library import GeoData
@@ -739,6 +740,29 @@ def preprocess(df):
             "file": "Bocklandt.csv",
         },
     },
+    "MiAge": {
+        "year": 2018,
+        "species": "Human",
+        "tissue": "Blood",
+        "source": "https://doi.org/10.1080/15592294.2017.1389361",
+        "output": "Mitotic Age (Cell Divisions)",
+        "model": {
+            "type": "MiAgeModel",
+            "file": "MiAge.csv",
+        },
+    },
+    "Bohlin": {
+        "year": 2017,
+        "species": "Human",
+        "tissue": "Cord Blood",
+        "source": "https://doi.org/10.1186/s13059-016-1063-4",
+        "output": "Age (days)",
+        "model": {
+            "type": "LinearMethylationModel",
+            "file": "Bohlin.csv",
+            "transform": lambda sum: sum + 277.2421,
+        },
+    },
     "PCHorvath1": {
         "year": 2022,
         "species": "Human",
@@ -1626,5 +1650,139 @@ def __getattr__(self, name):
         return getattr(self.clock, name)
 
 
+class MiAgeModel:
+    """
+    MiAge (Mitotic Age) clock implementation.
+
+    Based on Youn & Wang (2018): "The MiAge Calculator: a DNA methylation-based
+    mitotic age calculator of human tissue types."
+
+    MiAge estimates the number of cell divisions (mitotic age) using an optimization
+    approach with site-specific parameters (b, c, d) for 268 CpG sites.
+
+    Formula: For each sample, find n (mitotic age) that minimizes:
+        sum((c + b^(n-1) * d - beta)^2)
+    where beta is the observed methylation value for each CpG site.
+    """
+
+    def __init__(self, parameter_file):
+        """
+        Initialize MiAge model with site-specific parameters.
+
+        Parameters
+        ----------
+        parameter_file : str
+            Path to CSV file containing CpG, b, c, d columns
+        """
+        params = pd.read_csv(get_data_file(parameter_file))
+        self.params = params.set_index("CpG")
+        self.b_params = self.params["b"]
+        self.c_params = self.params["c"]
+        self.d_params = self.params["d"]
+        self.cpg_sites = self.params.index.tolist()
+
+    @classmethod
+    def from_definition(cls, clock_definition):
+        """Create MiAge model from clock definition"""
+        model_def = clock_definition["model"]
+        return cls(model_def["file"])
+
+    def _optimize_sample(self, beta_values):
+        """
+        Optimize mitotic age for a single sample using scipy.optimize.
+
+        Parameters
+        ----------
+        beta_values : numpy.ndarray
+            Methylation beta values for CpG sites
+
+        Returns
+        -------
+        float
+            Estimated mitotic age (number of cell divisions)
+        """
+        # Filter to valid (non-NaN) values
+        valid_mask = ~np.isnan(beta_values)
+        beta_valid = beta_values[valid_mask]
+        b_valid = self.b_params.values[valid_mask]
+        c_valid = self.c_params.values[valid_mask]
+        d_valid = self.d_params.values[valid_mask]
+
+        def objective(n):
+            """MiAge objective function: sum((c + b^(n-1) * d - beta)^2)"""
+            return np.sum(
+                (c_valid + b_valid ** (n - 1) * d_valid - beta_valid) ** 2
+            )
+
+        # Use multiple starting points for robustness
+        best_result = None
+        best_fun = np.inf
+
+        for start in [100, 500, 1000, 2000]:
+            result = minimize_scalar(
+                objective,
+                bounds=(10, 10000),
+                method="bounded",
+                options={"xatol": 1e-8},
+            )
+            if result.fun < best_fun:
+                best_fun = result.fun
+                best_result = result
+
+        return best_result.x
+
+    def predict(self, geo_data):
+        """
+        Predict mitotic age for methylation samples.
+
+        Parameters
+        ----------
+        geo_data : GeoData
+            Object containing methylation data
+
+        Returns
+        -------
+        pd.DataFrame
+            Predicted mitotic ages for each sample
+        """
+        methylation_data = geo_data.dnam
+
+        # Get shared CpG sites
+        shared_sites = methylation_data.index.intersection(self.cpg_sites)
+
+        if len(shared_sites) == 0:
+            raise ValueError("No overlapping CpG sites found for MiAge clock")
+
+        if len(shared_sites) < len(self.cpg_sites) * 0.5:
+            print(
+                f"WARNING: Only {len(shared_sites)}/{len(self.cpg_sites)} "
+                f"CpG sites available ({100*len(shared_sites)/len(self.cpg_sites):.1f}%)"
+            )
+
+        # Align data with model CpGs (keeping only shared sites)
+        # Need to reindex params to match shared sites order
+        shared_params = self.params.loc[shared_sites]
+        self.b_params = shared_params["b"]
+        self.c_params = shared_params["c"]
+        self.d_params = shared_params["d"]
+
+        X = methylation_data.loc[shared_sites].values
+
+        # Calculate MiAge for each sample
+        predictions = []
+        for i in range(X.shape[1]):
+            beta_sample = X[:, i]
+            miage = self._optimize_sample(beta_sample)
+            predictions.append(miage)
+
+        return pd.DataFrame(
+            predictions, index=methylation_data.columns, columns=["Predicted"]
+        )
+
+    def methylation_sites(self):
+        """Return list of required CpG sites"""
+        return self.cpg_sites
+
+
 def single_sample_clock(clock_function, data):
     return clock_function(data).iloc[0, 0]
diff --git a/biolearn/model_gallery.py b/biolearn/model_gallery.py
index 34fc25b..2a4f32a 100644
--- a/biolearn/model_gallery.py
+++ b/biolearn/model_gallery.py
@@ -11,6 +11,7 @@
     EpiTOC2Model,
     AltumAgeModel,
     PCLinearTransformationModel,
+    MiAgeModel,
 )
 from biolearn.imputation import (
     hybrid_impute,
@@ -36,6 +37,7 @@ class ModelGallery:
         "EpiTOC2Model": EpiTOC2Model.from_definition,
         "AltumAgeModel": AltumAgeModel.from_definition,
         "PCLinearTransformationModel": PCLinearTransformationModel.from_definition,
+        "MiAgeModel": MiAgeModel.from_definition,
     }
 
     def __init__(self, models=model_definitions):
diff --git a/biolearn/test/data/expected_model_outputs/MiAge.csv b/biolearn/test/data/expected_model_outputs/MiAge.csv
new file mode 100644
index 0000000..62a2c3e
--- /dev/null
+++ b/biolearn/test/data/expected_model_outputs/MiAge.csv
@@ -0,0 +1,11 @@
+id,Predicted
+GSM1009660,690.7299170623446
+GSM1009661,593.7206748761973
+GSM1009662,731.6428850713417
+GSM1009663,560.0016711692683
+GSM1009664,496.7881567481185
+GSM1009665,405.57820161202085
+GSM1009666,507.01735989703303
+GSM1009667,442.095405784304
+GSM1009668,618.8602361353671
+GSM1009669,583.0866615952626