In order to enhance both FEATURE's functional coverage and the performance of 1D motifs, we developed an extension to FEATURE, called the SeqFEATURE, that transforms sequence-based models into structure-based ones [ 20, 35].
Based on the transform sequence given by 2DIR analysis, this transformation of helical chain structures is found to be initiated with the motion of side chains and followed by the movement of main chains.
With the help of the transformed sequence { x i * }, we can deduce the following conclusion.
Note that it suffices to show that the transformed sequence { x i * } converges to 1.
Then the (transformed) sequence, Ax := (y n ), is denoted by y n : = ∑ k = 1 ∞ a n k x k, where it is assumed that the series on the right converges for each n∈ℕ.
This implies that the transformed sequence (the RPP) appears to be a stationary Poisson process (uniformly distributed occurrence times) with unit intensity (occurrence rate) if the model is sufficiently correct, but it appears to be heterogeneous otherwise.
To this end, we introduce a simple transformed sequence to construct a contraction to prove the convergence of solutions, and apply this way solving a class of general equation.
Epithelial cell transforming sequence 2 (ECT2) is a guanine nucleotide exchange factor for Rho family GTPase, which has been implicated in the malignant phenotype of human cancers.
Anatomical T1-weighted images were acquired using a 3-D MDEFT (modified driven equilibrium Fourier transform) sequence.
At the end of the functional runs, whole-brain structural scans were acquired using a Modified Driven Equilibrium Fourier Transform sequence.
With cutting-edge technologies the authors identified the epithelial cell transforming sequence 2 oncogene (ECT2) as a marker of invasive adenocarcinoma.
