Ross A. Lippert

TL;DR: The analysis of WGSA shows 97% order and orientation agreement with NCBI Build 34, where most of the 3% of sequence out of order is due to scaffold placement problems as opposed to assembly errors within the scaffolds themselves.

TL;DR: If the authors can solve a single supervised RLS problem, they can search for a good regularization parameter λ at essentially no additional cost, and this will be useful to people trying to learn more about linear algebra manipulations in the machine learning context.

TL;DR: Several other haplotypes in Ashkenazi Jews in both ESR1 and ESR2 that may elevate susceptibility to breast cancer are defined and genetic epidemiology study replication and functional assays of the haplotypes should permit a better understanding of the role of steroid receptor genetic variants and breast cancer risk.

TL;DR: This work presents a new method for optimally selecting minimum informative subsets ofSNPs, also known as "tagging" SNPs, that is efficient for genome-wide selection and shows that the number of tagging SNPs selected is substantially smaller than previously reported using block-based approaches and that selecting taggingSNPs optimally can result in a two- to threefold savings over selecting random SNPs.

TL;DR: Some of the computational approaches that have been taking for determining haplotypes and new approaches are suggested to reduce the complexity of studying SNPs.