Due to these inherent challenges, a comprehensive approach is needed to refine the contributions of the MHC to genetic risk for MS that includes a large and well-characterized dataset, dense concentration of markers, and appropriate methods to control for the extensive LD across the region.
In this study, the classical kNN classification algorithm has been refined so that the contribution of each of the k neighbors is weighted according to distance to the query point, giving greater weight to closer neighbors [18, 112].
By using an inducible system, the timing and the proportion of cells that undergo genetic recombination can be refined, such that the contribution of specific cells to tissues at defined developmental time points can be interrogated at clonal densities.
Summary: The work presented in this paper represents the first large study to conclusively associate de novo copy number variations with autism spectrum disorders (ASD), paving the way for several follow up efforts (some led by MAGNet investigators) that have further refined our understanding of the contribution of de novo mutations to the etiology of ASD.
We have further refined this approach to evaluate the contribution of scatter separated from refractive errors and higher order aberrations, with the main intention to classify the degree of cataracts.
In addition, since the level of confidence in markers will vary, these choices should be further refined by taking into account the contribution of each shared, dropped or included marker.
The use of datasets newly available in public repositories and the contribution of the scientific community to our open access model will contribute to refine, adjust and augment the CPN model.
This is an important first step towards developing and further refining gene-expression based assays that can be used to determine the contribution of dose-rate to overall dose.
When considering the contribution of DEVGs on pathway's dysregulation, it is necessary to refine the conventional approach to take into account the DEGs and DEVGs together.
Table 3 shows that the contribution of top 10 server predictors whose models were selected by MULTICOM to refine to generate the final predictions.
