Sentence examples for methods to capture information from inspiring English sources

Given the wide range of formal methods that are available to engage with users in the MDDD process, manufacturers tended to use only a very limited range of methods to capture information from users and patients.

Specifically, the consensus of GSEA-derived pathway rankings is used as an external benchmark pathway ranking, exploiting the capability of GSEA methods to capture information from gene expression levels and combining two diverse GSEA approaches (see below).

Landmark-based geometric morphometric methods were used to capture information about shape, by obtaining the x and y coordinates of homologous landmarks.

Using mixed methods, the questionnaire was designed to capture information in 5 relevant areas: a) general facility description, overview of services provided and human resources for health (HRH) capacity, b) HIV testing and counselling (HTC) services, c) Prevention of Mother to Child Transmission of HIV (PMTCT) services, d) ART services, e) laboratory services.

Prospective student surveys were most frequently used to capture information in relation to method of travel (Buliung et al., 2011, Garrard and Crawford, 2010, Hinckson and Badland, 2011, Hinckson et al., 2011, Mammen et al., 2014b, McDonald et al., 2013, Wen et al., 2008).

The immense growth in the volume of research literature and experimental data in the field of molecular biology calls for efficient automatic methods to capture and store information.

In this study, we propose segmented auto covariance and segmented distribution feature extraction methods to capture more local sequence order information from evolutionary and structural profiles.

We first investigate the effectiveness of our proposed feature extraction methods to capture local and global discriminatory information from the PSSM.

By proposing segmented distribution and segmented auto covariance feature extraction methods to capture local and global discriminatory information from evolutionary profiles and predicted secondary structure of the proteins, we are able to enhance the protein structural class prediction performance significantly.

In this study we propose segmented distribution and segmented auto covariance feature extraction methods to capture local and global discriminatory information from evolutionary profiles and predicted secondary structure of the proteins.

We combined different methods to capture a broad picture of the clinical information systems and their capabilities instead of focusing only on (technical) data quality.