We study a class of growth algorithms for directed graphs that are candidate models for the evolution of genetic regulatory networks.
DiSE-growth, a tree-based (pattern-growth) algorithm for mining DIverse Social Entities, is proposed and experimentally assessed in this paper.
In the limit of a bottle-brush polymer with a rather sti_ backbone (straight rigid backbone), we generalize the variant of the biased chain growth algorithm, the pruned-enriched Rosenbluth method, for simulating polymers with complex architecture, from star polymers to bottle-brush polymers, on the simple cubic lattice.
This is key to a consistent growth algorithm.
As a reference, we use the progressive edge growth (PEG) algorithm for the design of unstructured LDPC codes and compare its performance with that of four approaches for designing structured LDPC codes.
These CA growth algorithms are validated for simple thermal situations by comparing the predicted grain shapes with those deduced from analytical models.
Results: We developed PASSion, a pattern growth algorithm-based pipeline for splice site detection in paired-end RNA-Seq reads.
A random generation-growth algorithm is highlighted for reproducing multiphase microstructures, statistically equivalent to the actual systems, based on the geometrical and morphological information obtained from measurements and experimental estimations.
The program automatically recognized array features using a seeded-region-growth algorithm and reported, for each fluorescent channel, the average and standard deviation (SD) of pixel intensities within and around each array feature, the fraction of saturated pixels within each feature and the number of pixels of each feature, amongst other parameters (C. Cayla, personal communication).
The program automatically recognized array features using a seeded-region-growth algorithm and reported, for each fluorescent channel, the average and SD of pixel intensities within and around each array feature, the fraction of saturated pixels within each feature and the number of pixels of each feature, amongst other parameters (Cayla et al. in preparation).
The wearable was designed using generative growth algorithms.
