We investigate a 'policy matrix' representation, which assigns a score for each decision option independently and the option with the highest value is chosen, for one-dimensional online bin packing.
We constructed a context-transition scoring matrix, M, which assigns to each pair of labels a score that reflects the likelihood of the pair in known pathways.
Our techniques are based on calculating, for a given scoring matrix, the quantile function, which assigns a probability, or p, value to each segmental score.
This property is not well captured by the RMSD measure, and therefore, we also compute how well the distance matrix is recovered with the distance error, which assigns most of the weight to long distances.
The score assigned by Conseq was validated using PolyPhen (http://www.bork.embl-heidelberg.de/polyPhen/), which assigns scores, derived from likelihood matrices, as "benign", "possibly damaging", "probably damaging", or "unknown".
The approach uses the similarity matrix computed from various feature descriptors and a probabilistic hypergraph, which assigns each vertex to a hyperedge in a probabilistic way.
Further analysis was also conducted with HBV STAR analysis, which assigns HBV genotypes by using a position-specific scoring matrix (www.vgb.ucl.ac.uk/star.shtml).shtml
*, which assigns values to data items.
The error matrix, which can assign only symmetric errors, then becomes essentially meaningless.
Secondly, based on this condition, an algorithm for determining the required control gain matrices of acceleration and displacement feedback, which assign the desired eigenstructure, is developed.
In case all reference values for a certain element are 0 or positive, the respective element value is set to 0. In addition, a weighting matrix is generated which assigns weighting factors to the elements depending on how often the respective interaction occurs in the reference PADIFs (for instance, if it occurs in four of ten complexes, the weighting factor is 0.4).
