As described above, a gene gene interaction network can be encoded as an undirected graph G V, E) where V represents the genes and edges (i, j) ∈ E are weighted by a weight matrix W, whose element w ij represents the interaction strength between gene i and gene j.
1) Gene Gene Interaction: The gene gene interaction networks are encoded as an undirected graph G V, E) where V stands for the genes and edges (i, j) ∈ E are weighted by a weight matrix W, whose element w ij is the weight of the edge (i, j) ∈ E which represents the strength of interaction between gene i and gene j using two sources of gene gene interaction networks described below.
Assuming the i th relay multiplies its received signal by a weight matrix W i and forwards the resulting vector, x i, to the destination, thus x i = W i y i = W i H i x + n i = W i H i x + W i n i. (2) P i out = E x i 2 = E W i H i x + W i n i 2 ≤ P r i, (3).
The 64-channels by time matrix of EEG data, X, was transformed into a matrix of 64 independent component activations by time, U, by premultiplying X by a weight matrix, W, of unmixing coefficients, U = WX.
Formally, we describe the disease-disease similarity network by a weight matrix D = (d ij ) l × l, where l is the number of diseases and d ij is the weight of the edge between the ith and jth diseases or 0 if the edge is absent.
We describe the tissue-specific PPI network by a weight matrix P = (p ij ) m × m, where m is the number of proteins and p ij is the weight of the edge between the ith and jth proteins or 0 if the edge is absent.
In DCA, the compensation for depth is achieved by introducing a weight matrix within L2 regularization to counter-balance the reduced sensitivity of measurement at deeper depth.
Sverdlov et al. [ 18] constructed a dynamic model of intron insertion by using a weight matrix for the proto-splice sites and inserting introns with a probability proportional to the weight matrix score.
X. F. He et al. [24] present the locality preserving projections (LPP) by means of a weight matrix (called heat kernel).
Since the integrative genomic model aims to represent gene expression regulated by the combinations of SNP, CNV, and DM, the matrix C can be defined as (3) C = 0 0 0 1 0 0 0 0 1 0 0 0 0 1 0 1 1 1 0 1 0 0 0 1 0. We further consolidate the model by (1) introducing a weight matrix of the correlation for the balance of the model and (2) providing discriminant analysis in the integrative genomic model.
The quant _scale _code specifies a quant _scale which is further weighted by a weighting matrix, W, to obtain the quantization stepsize matrix (Equation 17).
