Sentence examples for advanced statistical approaches from inspiring English sources
An increasing number of digital records, big data analyses and advanced statistical approaches [ 33] facilitate this endeavor.
The richness of BRFS data could offer such information [ 5], although, so far, rarely has it been properly addressed with sufficiently advanced statistical approaches.
Advanced statistical approaches such as random forests may be helpful in identifying interacting genes in GWA studies, as demonstrated with respect to rheumatoid arthritis susceptibility [23].
Advanced statistical approaches to outlier detection in proteomics data have focused either on the identification of outlier spectra maps (Rudnick et al., 2010; Schulz-Trieglaff et al., 2009) or on peptide/protein abundances independent of LC-MS run behavior (Cho et al., 2008; MacCoss et al., 2003; Xia et al., 2006).
This makes it highly suitable for the calculation of a wide range of similarity measures for use in virtual screening and for the investigation of quantitative structure activity relationships in combination with advanced statistical approaches such as self-organizing maps, support vector machines and neural networks.
Case study applications of these advanced statistical approaches will identify potential strengths and weaknesses of these approaches and their significance for risk characterization.
A further advanced statistical approach, central composite design, found the optimum levels of the screened variables as follows (g l−1): glycerol 17.6, glutamic acid 59.6; yeast extract 2.7; K2.7O4 2.7.
The advanced statistical approach can flexibly examine effects of temperature on mortality.
An advanced statistical approach was adopted to deal with missing data other than the outcome variable to correct for potential biases or overestimation of diagnostic values and multiple comparisons.
When transferring function solely based on protein sequences, more sophisticated approaches, e.g. using advanced statistical frameworks [ 9], are needed to ensure high prediction quality.
The latter type of approaches (hardware-heavy) could be divided into two types of approaches: beamforming-based techniques and advanced statistical techniques.
