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A Bayesian binary regression algorithm was then used on the training set to generate the signature.
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The binary logistic regression algorithm was used for different combinations, and the predicted probability was produced and used as a new variable.
For the used binary regression algorithm, previous studies have shown that a feature set of one to two hundred top genes is adequate for a simple two category problem [ 6, 7].
These improvements made the binary regression algorithm more robust to gene selection and the number of genes used.
Binary regression models were applied for the multivariate analysis.
The E-M algorithm was used to estimate haplotype frequencies, and haplotype-based association analysis was conducted using binary logistic regression (additive model).
All binary search algorithms are synchronised [16].
To calculate the receiver operator characteristics, we used the probability function generated by the binary logistic regression algorithm.
Further, the algorithm is only efficient for binary rooted trees.
Binary logistic regression was applied [18].
Binary logistic regression was selected to model the hypotheses.
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