1. Logistic Regression: To classify gene pairs

2. Introduction
Linear Regression Classifier
Bio-python Libraries
Gene pairs classification
Into classes:
OP (if they belong to the same Operon)
NOP (otherwise)

3. genetic structure that contains one or more structural genes
Operon
genetic structure that contains one or more structural genes
Associated with each Operon are promoter and opertor sequences
Classification aim:
To identify if genes within a gene pair belong to the same Operon
illustration from

4. Logistic Regression Model
A set of input (predictor) variables
Distance between the genes
Gene expression score
Logit Score
𝑆= β 0 + β 1 𝑥 1 + β 2 𝑥 2

5. Training the model
Focus on Bacillus Subtilis Operons
The training data gathered from Operon DB
located at
Finding values for the beta coefficients
Done through MLE of probabilities (class OP vs class NOP given the data)

6. Training using the entire dataset yields:
Model Accuracy
Training using the entire dataset yields:
To calculate the Accuracy of the model
10-fold cross validation
Leave-one-out cross validation
𝑆= − 𝑥 𝑥 2

7. Model Testing Results (10-fold cross validation)
Average Type I error rate (False positive error rate)
19%
Average Specificity (probability that a pair of class NOP are classified correctly)
0.81
Average Type II error rate (False negative error rate) 4%
Average Sensitivity (probability that a pair of class OP are classified correctly)
0.96

8. Model Testing Results (Leave-one-out cross validation)
Accuracy = 90%
Sensitivity = 94%
False -ve rate = 6%
Specificity = 82%
False +ve Rate = 18%

9. Conclusions & Notes
Classifier performed well
More than two variables may be needed to improve performance
Classifier works on Bacillus Subtilis genes only
Due to the difference in gene length for different organisms
Operon DataBase (dataset source)
Uses 5 variables for classification (improves accuracy)
Aims to have all known operon information
Therefore has a large training set for multiple organisms
Allows web users to perform gene pair classification
Located at

10. Thank You
Questions ?

11. Refrences 1. Operon. from http://en.wikipedia.org/wiki/Operon
2. Garson G. Logistic regression from
3. Hoon M. The logistic regression model. from
4. Okuda S. Operon DataBase.
5. Schneider J. Cross validation from
Illustration: