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We applied this algorithm to 18 yeast time-series microarray datasets.
We applied this algorithm to published siRNAs, and could classify effective and ineffective siRNAs with 90.6%, 86.2% accuracy, respectively.
We applied this algorithm to the basal forebrain cholinergic system, which consists of a diffuse but inhomogeneous population of neurons (Zaborszky, 1992).
We applied this algorithm to eight rat neurons covering the entire synaptic loop of the hippocampal formation (entorhinal cortex, dentate gyrus, CA3, CA1, and subiculum).
For this, we have developed the rat grooming analysis algorithm (based on ethological analysis of incorrect transitions contrary to the cephalocaudal rule, interrupted grooming activity and the assessment of the regional distribution of grooming) and applied this algorithm to the light-exposed (high stress) and dark-exposed (low stress) groups of rats.
Here, we have applied this algorithm to identify peptide epitopes (of nine amino acids, the common length) from the sequence of the cancer-testis antigen KU-CT-1, based on the potential of these peptides to bind to the human MHC molecule HLA-A2.
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We then apply this algorithm to obtain specific results for planar graphs and Euclidean graphs.
But by applying this algorithm to business registration records, the researchers can get a reasonable measure of the entrepreneurial quality of a city, state, or nation.
We apply this algorithm to several matrices that arise in applications.
We further apply this algorithm to the coupling of flows with moving rigid bodies.
We present an iterative refinement algorithm for computing discrete abstractions of a class of symbolic hybrid automata, and we apply this algorithm to a model of multiple cell Delta-Notch protein signaling.
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