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The total number of possible combinations can be represented as binomial coefficients and they can be derived from Pascal's triangle.
Drugs or drug combinations can be represented as their corresponding features described by multiple drug properties.
Domains and their ability to form different combinations can be represented as a graph, allowing the usage of network-analysis tools.
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The DS combination can be represented for Q information sources by the following orthogonal rule: m ( H i ) = m 1 ( H i ) ⊕ m 2 ( H i ) ⊕... ⊕ m Q ( H i ) (3).
Then drug combination can be represented as the union of two probabilistically independent events.
Thus, each drug combination can be represented by a vector in a 465 D (dimensional) space, that is, each feature is deemed as a dimension.
Thus, each possible combination can be represented as a 16-dimensional profile where, for instance, {XXXXXXXXXXXXXXXX} denotes a genotype profile that contains only minor variants.
For simplicity, we assume that this probability is the same for all the node-channel combinations and can be represented by p. For smaller values of p, few or none of the channels are common between nodes.
Then the combination of charges can be represented as: X r bin ≜ X r H bin X g bin ≜ X g H bin X b bin ≜ X b H bin.
So the difference between the given skull S gv and the model combination skull S md can be represented as the square module of the subtracting vector as following form: E = ∥ S md − S gv ∥ 2 = ( S md − S gv ) T ( S md − S gv ) (11).
Consequently, the cell labeling associated with the two possible combinations after the first division can be represented by Figure 4 from Berkovich and Bloom [ 6].
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