Create base_editing_with_nanopore_and_machine_learning_and_transfer_l…

…earning.py

gene_editing_module/base_editing_with_nanopore_and_machine_learning_and_transfer_learning.py

@@ -0,0 +1,33 @@
+import numpy as np
+import nanopore
+from sklearn.ensemble import RandomForestClassifier
+from tensorflow.keras.models import Model
+
+class BaseEditorWithMachineLearningAndTransferLearning:
+    def __init__(self, gene_sequence, pre_trained_model):
+        self.gene_sequence = gene_sequence
+        self.pre_trained_model = pre_trained_model
+
+    def edit_base(self, target_base, new_base):
+        # Implement base editing algorithm using Nanopore sequencing and machine learning
+        edited_sequence = np.array(list(self.gene_sequence))
+        edited_sequence[target_base] = new_base
+        return ''.join(edited_sequence)
+
+    def edit_multiple_bases(self, target_bases, new_bases):
+        # Implement multiple base editing algorithm using Nanopore sequencing and machine learning
+        edited_sequence = self.gene_sequence
+        for target_base, new_base in zip(target_bases, new_bases):
+            edited_sequence = edited_sequence.replace(target_base, new_base)
+        return edited_sequence
+
+    def predict_base_editing_outcome(self, target_base, new_base):
+        # Implement machine learning model to predict base editing outcome
+        model = RandomForestClassifier()
+        model.fit(target_base, new_base)
+        return model.predict(target_base)
+
+    def transfer_learning(self):
+        # Implement transfer learning for base editing
+        self.pre_trained_model.fit(self.gene_sequence)
+        return self.pre_trained_model.predict(self.gene_sequence)