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Here, memory sequences are described as sequences of random activity patterns ξ that are binary vectors of dimension N, ξ ∈ { 0, 1 } N. A memory sequence of length Q is an ordered occurrence of activity patterns ξ 1 → ξ 2 → ⋯ → ξ Q.
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The hash function H H ∘ F ( C ) → ℤ 2 h. is used to project the set of features to a binary vector of the dimension h.
We obtained a total of 14,831 domains from the Pfam database (Version 27.0) [ 33] and characterized each human protein using a binary vector of such number of dimensions.
Focusing on human pathways in the KEGG database [ 30] and discarding diseases-related ones to avoid biases towards well-studied diseases, we obtained a total of 238 pathways and characterized each human gene using a binary vector of such number of dimensions.
The internal structure of this architecture is as follows: words are first represented via a binary encoding as vectors of dimension (dictionary size), i.e. these vectors are all zeros apart from one 1, indicating the word's index into the dictionary.
For a given subject corresponding vectors describing each of the six observed SNPs were concatenated together, resulting in a final binary feature vector of dimension 18.
Each A u is a binary vector of length.
Equivalently, MCC can be defined as the Pearson's correlation coefficient of the binary vector of class labels compared to the binary vector of predicted class labels.
A binary vector of length n therefore represents a single individual in the GA population.
Next, we expand hashing values organizing the string into a new binary vector whose dimension is much smaller than that of the original fingerprint.
The are converted into vectors of dimension.
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