Sentence examples for reconstructing a set of from inspiring English sources

We are interested in reconstructing a set of networks 𝒢(1),…, 𝒢(N ) that are not independent of each other, but are related by a genealogy over their respective host cell-types, thereby constituting a tree evolving network.

This work proposes a family of greedy algorithms to jointly reconstruct a set of vectors that are (i) nonnegative and (ii) simultaneously sparse with a shared support set.

We present an approach based on Boolean analysis to reconstruct a set of parsimonious networks from gene disruption and over expression data.

These were used as input in PhyML [39] to reconstruct a set of maximum likelihood trees by estimating and implementing the GTR + I + G nucleotide substitution model.

On each iteration the algorithm must evaluate how well the given site(s) can reconstruct a set of BSPMs.

We also reconstructed a set of fixed synonymous substitutions for the same set of human genes (Table  1) using multiple alignments of orthologs from humans, chimp and gorilla.

The process begins by evaluating how well each of the 192 available recording sites can be used individually to reconstruct a set of BSPMs.

In the previous section, we have seen how to sample and reconstruct a set of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$K$$\end{document} K Diracs.

In CS, the sampling and compression operations are combined into a low complexity compressed sampling[4], in which compressible signals can accurately be reconstructed from a set of random linear measurements by using nonlinear or convex reconstruction algorithms[6, 7].

First, a road network is reconstructed into a set of connected communities.

The denoised dataset will be reconstructed using a set of feasible propagation speed values, defined as (21).