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The proposed equalizer consists of several simple small-scale functional link artificial decision feedback recurrent neural network (FLADFRNN) modules with less computational complexity.

Considering comprehensive correlation degree among design parameters including their functional relevance, connection relevance and physical relevance, we clustered and grouped design parameters into modules with less dependent degree in design structure matrix.

The largest possible modules with less than 1% intra-cluster anti-correlated links were manually dissected.

Modules with less than 3 genes were discarded from further analyses.

To reduce the potential bias caused by a small sample size, we excluded those transcriptional modules with less than 10 genes and selected 82 transcriptional modules for subsequent analyses.

Similar(55)

Of a total of 8462 GO modules, those with less than three components (4284 GO terms) or more than 200 (274 GO terms) were discarded from the analysis given the difficulties for building empirical random distributions outside of this range.

Now, let ( n > 1 ) and assume that the assertion is true for all modules with length less than (n).

Let (alpha > 1) and every Dedekind finite module with c.u.dim less than (alpha ) be decomposed to finitely many indecomposable modules.

In patients, functional DD and anatomical between-module connectivity were positively correlated (regression B coefficient 95% CI 0.171 to 2.482, P =  0.025, Fig. 5): indicating that lower, more disturbed anatomical between-module connectivity correlated with less dysfunctional DMN+.

We then output all motif modules with q-value less than 0.01 as motif pairs with significant orders.

If it is low, then co-modules will be bigger, with less similar genes.

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