Sentence examples for methods reviewed by from inspiring English sources

The methods reviewed by Vicente et al. [3] all focus on cosegmenting two images.

The development of genotyping-by-sequencing (GBS) methods (reviewed by Davey et al. 2011; Poland and Rife 2012) has accelerated the use of genomic data in population genetic studies of nonmodel organisms.

Such conservation status has been employed in annotating protein sequences by different methods reviewed by Ouzounis et al. 12 Though many of these methods fare well at assigning an unknown protein at a family level, the accuracy fails when a classification is required at a subfamily level.

Examples include the development of novel stem cell techniques, organotypic three-dimensional (3D) cell models, in vitro disease models, and an increasing number of in vitro cell-based "omics" and in silico methods (reviewed by Adler et al. 2011; Wobus and Löser 2011).

The methods reviewed by Cordell (2009) include novel approaches such as combinatorial partitioning (Culverhouse et al., 2004; Nelson et al., 2001) and logic regression (Kooperberg et al., 2001; Kooperberg and Ruczinski, 2005) and machine learning approaches such as random forests (RFs).

This and various other methods that have been devised to identify functional residues in proteins, such as residue interaction graphs (Amitai et al. 2004) or statistical methods reviewed by Ahola et al. (2004) could reasonably be used in conjunction with structure-based alignment methods to determine reasonable alignments based on functional considerations.

To our knowledge none of the matrix methods reviewed here were accompanied by an accessible and widely available software implementation.

In the enterobacterium E. coli, a number of sRNAs have been predicted by computational methods (reviewed in [ 19]).

Regression has been successfully adapted to high-dimensional situations by penalization methods (review by Hesterberg, 2008), and penalized regression has been shown to outperform univariate and other multivariate regression methods in multiple genomic datasets (Bøvelstad et al., 2007).

The main challenges of analysing InSAR data include solving for this ambiguity and identifying or correcting the contributions that other factors, such as variations in satellite position or atmospheric composition, make to phase (InSAR methods are reviewed by Simons & Rosen, 2007).

More recent approaches to analyze time-series data, including regression, differential expression, discriminant and clustering methods, are reviewed by Coffey and Hinde [ 11].