Sentence examples for methods for class from inspiring English sources

We also discuss how accounting for peptide binding in multiple registers to class II MHC largely explains the systematically worse performance of prediction methods for class II MHC compared with those for class I MHC based on quantitative prediction performance estimates for peptide binding to class II MHC in a fixed register.

This approach started with the development of molecular typing methods for class II loci (e.g. PCR-SSP, direct or reverse PCR-SSO, PCR-SSCP, and SBT), and more recently for class I loci, HLA-Cw being the last gene studied with such typing strategies [3], [4], [5], [6], [7].

The sampling methods for class balancing aim to alter the distribution of the training data in order to increase the accuracy of its models [ 24].

As previously mentioned, the 450 k array includes probes designed to target UCSC CpG islands, as well as shores, shelves and non-island regions, which we refer to as the 'sea' [ 9] (Additional file 5A, see methods for class definitions).

Using two statistical methods for class prediction, we then identified sets of genes that distinguished DOX- and 5FU-treated cells and used these lists to predict the mechanism of etoposide (ETOP), a drug that is mechanistically similar to DOX.

We expanded the 450 k annotation by categorizing probes into four HIL classes: 1) HC probes, 2) IC probes, 3) ICshore probes (regions of intermediate-density CpG island that border HCs) and 4) LC probes (Additional file 5B, see methods for class definitions) [ 16, 18].

Wei and Chen [37] presented a numerical method called spectral methods for classes Volterra type integro-differential equations with weakly singular kernel and smooth solutions.

In this paper, we adapt methods for classing mountain pine beetle-induced tree mortality to detect spruce beetle-induced tree mortality from single-date high-resolution imagery and multi-date moderate-resolution data.

The approach works similarly with other bipartite graph inference methods for classes (i) and (ii) in terms of accuracy, but the approach with an aggregation scheme is quite powerful for class (iv) (Bleakley and Yamanishi, 2009), so the approach with pharmacological information could detect missing interactions in class (iv) with high accuracy.

This research paper addresses this requirement for developing a standard mix design method for Class F, low calcium fly ash based geopolymer concrete using Multivariate Adaptive Regression Spline (MARS) model.

We tested our method for class discovery on two data sets: a data set of 87 tissues, comprised mostly of small, round, blue-cell tumors (SRBCTs), and a data set of 22 breast tumors.