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?

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

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

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 Xi of the expression level Ei and reference level Ri is?
A.	Xi = $\frac{Log (E_i/R_i)}{\sqrt{\sum_{j=1,N} Log_{z-2} (E_j/R_j)}}$
B.	Xj = $\frac{Log (E_j/R_i)}{\sqrt{\sum_{j=1,N} Log_z (E_j/R_j)}}$
C.	Xi = $\frac{Log (E_i/R_i)}{\sqrt{\sum_{j=1,(N-1)} Log_z (E_j/R_j)}}$
D.	Xi = $\frac{Log (E_i/R_i)}{\sqrt{\sum_{j=1,N} Log_z (E_j/R_j)}}$
Answer» B. Xj = $\frac{Log (E_j/R_i)}{\sqrt{\sum_{j=1,N} Log_z (E_j/R_j)}}$

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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 Xi of the expression level Ei and reference level Ri is?
A.	Xi = \(\frac{Log (E_i/R_i)}{\sqrt{\sum_{j=1,N} Log_{z-2} (E_j/R_j)}}\)
B.	Xj = \(\frac{Log (E_j/R_i)}{\sqrt{\sum_{j=1,N} Log_z (E_j/R_j)}}\)
C.	Xi = \(\frac{Log (E_i/R_i)}{\sqrt{\sum_{j=1,(N-1)} Log_z (E_j/R_j)}}\)
D.	Xi = \(\frac{Log (E_i/R_i)}{\sqrt{\sum_{j=1,N} Log_z (E_j/R_j)}}\)
Answer» B. Xj = \(\frac{Log (E_j/R_i)}{\sqrt{\sum_{j=1,N} Log_z (E_j/R_j)}}\)

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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