Traditional heuristic mapping algorithms follow static procedures, thus cannot be optimized automatically, leading to sub-optimal ranking and embedding decisions.
The algorithm follows: 1.
Our BWA-SW algorithm follows this route.
The expression data was clustered using the self-organizing map algorithm as follows.
It is also possible to annotate CAZymes by the use of a gene prediction algorithm followed by mapping of conserved domains [ 43] or directly through the CAZy database [ 29].
Ten-minute static PET scans were then acquired, and images were reconstructed by an OSEM3D (three-dimensional ordered subsets expectation maximum) algorithm followed by MAP (maximization/maximum a posteriori) or FastMAP provided by Inveon Acquisition Workplace.
The initial genetic map for each chromosome was constructed using the greedy algorithm followed by a ripple function with a window size of six markers.
Some comments on this algorithm follow.
The original algorithm followed particle movement by determining the relative rotations and translations of individual particles in running averages of several movie frames with respect to a common reference map.
Following the MDS-HSI to HUI2 mapping algorithm reported by Wodchis et al. [ 27], we calculated, from the index MDS, for each study resident a summary MDS-HSI score and individual subscores for the six MDS-HSI dimensions.
