Sentence examples for methods will increasingly from inspiring English sources

25 years ago several persons within the German Chemical Society realized that computer methods will increasingly play a major role in the processing of chemical information.

We are only aware of one recent method that attempts to rigorously evaluate distant phylogenies, PHYRN, and we hope that such methods will increasingly be developed and validated (we have unsuccessfully approached the authors to try and use PHYRN).

As neuroimaging algorithms and technology continue to grow faster than CPU performance in complexity and image resolution, data-parallel computing methods will be increasingly important.

As such, these methods will become increasingly important for planning surveillance, control and response activities in the current and future stages of the eradication programme, as attention focuses towards reducing cases, and ultimately infections, to zero.

Given the recent policies mandating public disclosure of results for relevant registered trials [9], [10], information on trial methods will be increasingly important to reliably interpret the trial results.

Recombination methods will become increasingly useful in light of the explosion in genomic sequence data and potential for engineered proteins.

More accurate genotype imputation methods exist, e.g. [ 6– 9], and it is expected that such methods will become increasingly more appropriate as more genotypic data accumulate.

This type of information obtained by informatics methods will become increasingly important in accord with the increase of nucleotide sequences obtained from a wide variety of virus strains.

The development and evaluation of complex interventions has emerged as an important component of KT, so expertise in conducting non-traditional review methods will become increasingly important for researchers, teachers, and students.

It is expected that analysing new and existing data from public repositories such as Array Express (www.ebi.ac.uk/arrayexpress/), Gene Expression Omnibus (GEO) (www.ncbi.nlm.nih.gov/geo/) and the Connectivity Map (CMap) (www.broadinstitute.org/cmap/) using these methods will become increasingly popular in computational drug discovery (Iorio et al., 2012).

As data begin to appear from large-scale resequencing projects such as the 1000 Genomes Project in humans, the 1001 Genome Project in Arabidopsis, and the Drosophila Genetic Reference Panel Project in flies, understanding the practical application and limitations of these short-read methods will become increasingly important.