In SVMs (Support vector machines) Data points are log-transformed and normalized as in method A, where for N observations of a gene i, the log transform Xi of the expression level Ei and reference level Ri is?

X j = ( frac{Log (E_j/R_i)}{ sqrt{ sum_{j=1,N} Log_z (E_j/R_j)}} )

Xi = ( frac{Log (E_i/R_i)}{ sqrt{ sum_{j=1,N} Log_{z-2} (E_j/R_j)}} )

Xj = ( frac{Log (E_j/R_i)}{ sqrt{ sum_{j=1,N} Log_z (E_j/R_j)}} )

Xi = ( frac{Log (E_i/R_i)}{ sqrt{ sum_{j=1,(N-1)} Log_z (E_j/R_j)}} )

Xi = ( frac{Log (E_i/R_i)}{ sqrt{ sum_{j=1,N} Log_z (E_j/R_j)}} )

In SVMs (Support vector machines) Data points are log-transformed and

1.	In SVMs (Support vector machines) Data points are log-transformed and normalized as in method A, where for N observations of a gene i, the log transform X_i of the expression level E_i and reference level R_i is?
A.	X<sub>i</sub> = ( frac{Log (E_i/R_i)}{ sqrt{ sum_{j=1,N} Log_{z-2} (E_j/R_j)}} )
B.	X<sub>j</sub> = ( frac{Log (E_j/R_i)}{ sqrt{ sum_{j=1,N} Log_z (E_j/R_j)}} )
C.	X<sub>i</sub> = ( frac{Log (E_i/R_i)}{ sqrt{ sum_{j=1,(N-1)} Log_z (E_j/R_j)}} )
D.	X<sub>i</sub> = ( frac{Log (E_i/R_i)}{ sqrt{ sum_{j=1,N} Log_z (E_j/R_j)}} )
Answer» B. X<sub>j</sub> = ( frac{Log (E_j/R_i)}{ sqrt{ sum_{j=1,N} Log_z (E_j/R_j)}} )

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In SVMs (Support vector machines) Data points are log-transformed and normalized as in method A, where for N observations of a gene i, the log transform Xi of the expression level Ei and reference level Ri is?

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In SVMs (Support vector machines) Data points are log-transformed and normalized as in method A, where for N observations of a gene i, the log transform X_i of the expression level E_i and reference level R_i is?