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Statistical methods have been developed to infer the fine-scale structure of recombination rate variation from genome-wide scale data [4].
With large genome-wide scale data, this approach makes it feasible to discover higher-order interactions.
We developed genomeSIMLA [ 48] for simulating genome-wide scale data in population based case-control samples with a categorical outcome.
However, applying this entropy based method directly to the genome-wide scale data would be infeasible because of computationally intensive permutations.
Several ways of accounting for hidden population stratification have been proposed (genomic control (GC) correction, adjusting for ancestry-informative principal components (PCs)), but these approaches are only applicable in genome-wide scale data.
The permutation and exhaustive search schemes of the previous GGI methods are computationally too intensive to be employed in large genome-wide scale data set for high-order interactions.
Such strict new rules would replace a law from 1995, which was conceived as more of a legal directive that effectively limited the possibilities of wide-scale data mining for direct marketing or other commercial purposes.
The difficulty has been for projects that have been carried out at a time when wide-scale data sharing was not envisaged.
This raises a number of ethical issues as many of the principles and procedures in medical research are not designed for wide-scale data sharing.
eMERGE-I had three major aims: (i) use EMR data for robust electronic phenotyping, (ii) conduct genome-wide association studies (GWAS) using the phenotypes derived in the above-mentioned first aim, and (iii) explore the ethical, legal, and social implications associated with EMR-based GWAS and wide-scale data sharing.
This could be added to by wider-scale data collected from unmanned aerial vehicles (drones), and ultimately global data from satellites.
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