Specifically, under H0, for each given δ1 = δ2 = δ, the P-value of the observed statistic T is computed as below.
The P-value for observing m proteins having a PTM term can be computed as is shown below: (1) P -value = ∑ x = m min (K, n ) (K x ) (N − K n − x ) (N n ).
A progressively improving estimate of h avg is computed as explained below.
To avoid this information leakage, H m also applies masking by adding random values, ϕ m and ψ(m,r), which are computed as described below, and sends the resulting masked values to the service provider.
For a given measurement,y1 and based on the current estimate of the parameter, the expected value of x1 is computed as given below: x 1 k + 1 = E x 1 | y 1, p k (26).
Finally, the damage index of the yth story using the weighted beam and column damage index of each story is computed as given below: D y = ( D y B ) 2 + ( D y C ) 2 D y B + D y C (12).
For a given measurement,y1 and based on the current estimate of the parameter, the expected value of x1 is computed as given below: x 1 k + 1 = E x 1 | y 1, p k (26) This implies x 1 k + 1 = y 1 1 / 4 1 4 + p k 2 (27) Maximization step (M Step): From the expectation step, we use the data which were actually measured to determine the maximum likelihood (ML) estimate of the parameter.
Differences between two profiles (costs) were computed as detailed below.
For a set of n G genes, the inference score (Y) is computed as shown below (Li and Liang 2009): Y = –ln[ P G associated with both C and D| k, n G) P(no other G connects C and D| k, n G)].
