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In case the dimensionality of the double-mutant fitness matrix W is moderate, the matrix approximation problem can be treated using iterative procedures, which solve the weighted least-squares optimization problem, in which binary weights can be employed to ignore the effects of missing entries [58].
Alternatively, frequency-dependent binary weights can be used to exclude microphone pairs at the frequency bins where spatial aliasing might occur for those pairs, as done in [27].
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This binary weight can then be compared with the corresponding weight prediction made from the model signature, namely a 1 if the two genes are either both upregulated or both downregulated in response to the oncogenic perturbation, or -1 if they are regulated in opposite directions.
Weight can be an important factor.
For such sparse binary vectors, weight vectors can be learned via efficient optimization algorithms (Hsieh et al., 2008).
Binary observations can be considered as extreme censoring.
The uncoded binary DM can be summarized as follows.
The proposed binary classifier can be briefly described as follows.
Nevertheless, also non binary codes can be adopted.
Binary IS typing is easy to perform and binary profiles can be generated in a standardized fashion.
The heritability of binary phenotypes can be computed directly on the observed binary scale.
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