We test the method against a suite of linear and nonlinear tests to ascertain accuracy and stability of the scheme under a variety of conditions.
We have applied the sMBPLS method to a suite of genomic profiles from 230 ovarian cancer samples, including CNV, DM, microRNA and GE data.
We applied sMBPLS method to a suite of TCGA data including the CNV, DM, ME and GE data on 230 ovarian cancer samples.
We describe single cell cloning and drug selection procedures methods and a suite of lentiviral vectors to engineer hESC lines for visualization, tracking and purification of pluripotent stem cells and their differentiated cardiomyocyte derivatives.
For the real data, we validate all array-derived DMRs from the candidate methods on a suite of DMRs derived from whole-genome bisulfite sequencing called from the same DNA samples, using two separate phenotype comparisons.
Experimental results confirm the efficiency and effectiveness of the proposed method in monitoring a suite of process variables with different distribution characteristics, showing that the proposed RBFNN PCA method is a reliable scheme as an effective extension to the linear PCA method.
While tasks (er, steps) are central, Action Method comes with a suite of other ways to communicate and keep track of projects.
Finally, the effectiveness of the proposed method is verified by a suite of benchmark functions and practical engineering problems.
The method is applied to a suite of canonical test cases, and conservation, accuracy, and parallel performance are assessed.
Based in part on the emerging benchmark dose (BMD) method, the modified BMD method we used involves applying a suite of conventional mathematical models to tumor dose response data.
