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Exact(4)

In contrast to our expectations, however, the matrix approximation strategy could improve the detection of the negative SL and SS categories in the E-MAP data (Table 4).

Surprisingly, however, the matrix approximation strategy could improve the detection of those categories of BioGRID that were independent of the E-MAP data under the analysis (SL and SS; see Table 4).

Briefly, the matrix approximation strategy is based on the observation that most gene pairs in the large-scale genetic interaction screens have no significant interaction with each other [ 2, 3].

To improve the predictive power of the individual quantitative datasets, we further developed our computational matrix approximation strategy [ 34], and showed that it could transform the original fitness matrices so that these allow for better discrimination of not only negative but also the positive end of interaction spectrum from the background variability [ 31].

Similar(55)

To investigate whether the matrix approximation-based scoring strategy could enhance the between-approach comparability of the quantitative information encoded in the double-mutant fitness matrices, we next used the same estimation parameters and scoring functions defined in the previous within-approach evaluations [ 31].

We then proceed to study the expected error of matrix approximation.

However, the multitarget Bayes filter has no practical utility without an approximation strategy.

Suppose that A is a low-rank matrix approximation of D, where D and A are (m times n) matrices.

The proof is based on a regularization and approximation strategy designed for our time-dependent framework.

Recent work on fast matrix approximation may help speed up these calculations and save memory capacities [34], [35].

ROL and VPE developed and implemented the matrix approximation method.

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