The past year has witnessed substantial advances in understanding the genetic basis of
many common phenotypes of biomedical importance. These advances have been the result …

RandNLA: randomized numerical linear algebra Page 1 80 COMMUNICATIONS OF THE ACM
| JUNE 2016 | VOL. 59 | NO. 6 review articles DOI:10.1145/2842602 Randomization offers new …

A common method of minimizing errors in large DNA sequence data sets is to drop variable
sites with a minor allele frequency (MAF) below some specified threshold. Although …

Randomized algorithms for very large matrix problems have received a great deal of
attention in recent years. Much of this work was motivated by problems in large-scale data …

The statistical leverage scores of a matrix A are the squared row-norms of the matrix
containing its (top) left singular vectors and the coherence is the largest leverage score …

We reconsider randomized algorithms for the low-rank approximation of symmetric positive
semi-definite (SPSD) matrices such as Laplacian and kernel matrices that arise in data …

One popular method for dealing with large-scale data sets is sampling. For example, by
using the empirical statistical leverage scores as an importance sampling distribution, the …

Recent improvements in the speed, cost and accuracy of next generation sequencing are
revolutionizing the discovery of single nucleotide polymorphisms (SNPs). SNPs are …

Polar bears (PBs) are superbly adapted to the extreme Arctic environment and have become
emblematic of the threat to biodiversity from global climate change. Their divergence from …

Recent technology has made it possible to simultaneously perform multi-platform genomic
profiling (eg DNA methylation (DM) and gene expression (GE)) of biological samples …