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Alternately, we developed a tissue compartment analysis (TCA) method that allows quantifying the fractional volume of the different structures constituting tissue samples and solving the problem of tissue sample heterogeneity, and applied it to identify biomarkers.
Both co-clustering and template assignment identify small differences between similar samples, and solve the problem of enumerating an absent or very small population, e.g., 0 5 cells in a negative control.
We note that whereas the low-rank functional identification algorithm is formulated as nonlinear sampling using TEMs and solved using recent advances in low-rank matrix sensing, the other algorithms tested here rely on moment-based or likelihood-based methods that require a large number of samples to converge.
Students simply searched the Internet for missing information, unaware of the assumptions behind most data (i.e., sample sizes and confidence intervals) and solved the problem.
The idea is predicting the objective function and solution of the true problem by taking a sample size of N and solve the problem in Eq. (18) known as SAA problem.
RO algorithms generate a sequence of sample-path (SP) problems and solve these SP problems iteratively using a nonlinear optimizer.
The concept behind bootstrapping is to approximate a true sampling distribution by constructing a pseudo-sample and re-solving the DEA model for each unit with the new data.
First smaller sample problem solved by NSGA-II and resulted solutions compare by ɛ-constraint method with resulted solutions from model solving.
Finally, the problem of cloud type identification for the test sample was solved according to the similarity between the test sample and specific cloud type subspace.
To examine the model's suitability for use in dynamic simulations out of sample, we solved it forward for 50 years without any policy intervention.
First, CASP targets comprise a realistic sample of currently solved protein structures and exemplify the corresponding challenges for predictors.
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