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The Hamming distance H ( s →, s → ∗ ) between two Boolean vectors s → = ( s 1, …, s N ) and s → ∗ = ( s 1 ∗, …, s N ∗ ) with the same domain is the number of i with s i ≠ s i ∗.
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Before we will show that Equation (5) fits a branch and bound framework, we first make the observation that for the relation between and f the following holds: (6) where y1 and y2 are two Boolean vectors.
This criterion is defined as the Hamming distance between two Boolean response vectors.
Arvind and Vasudev [2] have introduced the notion of an approximate isomorphism between two Boolean functions f and g.
All possible Boolean combinations between two Boolean variables (minterms) X and Y describing the activation state of two binding sites X and Y can have a different interpretation to determine dependency between two binding sites, cooperativity and anti-cooperativity, and also binding sites functions (repression, enhancement).
First, two Boolean row vectors are introduced to characterize the discernibility matrix and reduct in VPRS.
We use two N bits Boolean vectors k and e, called knocked out genes and over expressed genes, respectively.
Cosine similarity between two protein structures represented using boolean vectors A, B is given below We refer to the similarity based on naive vector representation as FragSimilarity.
The MCC represents a Pearson correlation between two binary vectors.
Linear maps V → W between two vector spaces form a vector space HomF V, W), also denoted L V, W).
The problem amounts to finding in a specified class of Boolean functions one that complies with two given sets of "true" and "false" Boolean vectors, i.e., a function that takes the value 1 for each of the "true" vectors and 0 for each of the "false" vectors.
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