Sentence examples for methods for integrative from inspiring English sources

Epitope prediction tools were then reviewed followed by methods for integrative data analyses and network modeling.

Successful methods for integrative GRN inference must possess the following key properties: (i) The method must only include the part of the prior with support from the data.

We then give an overview of the bioinformatics methods for the analysis of next-generation sequencing data (whole-genome and exome sequencing), epitope prediction tools as well as methods for integrative data analysis and network modeling.

The average opinion when asked about methods for integrative analysis of multiple data types was slightly different as there was no clear consensus on the availability of proper tools (2.57).

Since microarray and sequencing based genome-wide expression data sets have been increasingly collected, it is necessary to further develop the computational and statistical methods for integrative data analysis studies.

iBFE is an effective and efficient method for integrative analysis of heterogeneous genomic data to accurately identify disease subtypes.

Second, we demonstrate the application of such methods for an integrative study on financial and sustainability aspects of a complex energy system.

In February 2013, the FP7 STATegra project (http://stategra.eu/) and the COST Action SeqAhead (http://seqahead.eu/), two EU-funded initiatives on the bioinformatics of high-throughput data, organized in the city of Barcelona the "Workshop of Omics and Data Integration", with the aim of reviewing current technologies on omics data production and the available methods for their integrative analysis.

To tackle these limitations, we propose a novel Multiblock Discriminant Analysis (MultiDA) method for the integrative genomic study.

For our future research, it will be useful to extend our proposed method for an integrative analysis of data with multiple sample groups.

The most straightforward method for an integrative analysis across datasets is to first compute the age-association genes per dataset and subsequently inspect the overlap of significant results.