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Table 2 Work flow of LR

From: Identifying Alzheimer’s disease-related proteins by LRRGD

Work flow of LR
Step 1. Constructing a prediction function
\( {h}_{\theta }(x)=g\left({\theta}^Tx\right)=\frac{1}{1+{e}^{-{\theta}^Tx}} \)
θis regression variable, x is independent variable
Step 2. Construction loss function
\( J\left(\theta \right)=-\frac{1}{m}\sum \limits_{i=1}^m\left[{y}_i\log {h}_{\theta}\left({x}_i\right)+\left(1-{y}_i\right)\log \left(1-{h}_{\theta}\left({x}_i\right)\right)\right] \)
 y is true similarity, m is the number of sample
Step 3. Newton method for getting the minimum J(θ)
\( \theta \leftarrow \theta -\frac{l^{\hbox{'}}\left(\theta \right)}{l^{\hbox{'}\hbox{'}}\left(\theta \right)} \)
l(θ) is maximum likelihood function