Sentence examples for build models based on from inspiring English sources

The increasing volume of chemogenomics data offers exciting opportunities to build models based on Big Data.

This framework also serves as a tool for practitioners to build models based on goals for students.

In this paper, we build models based on an innovative approach, which do not need to make the assumption of identical distribution.

This semi-supervised methodology allows us to build models based on weakly labelled data while incrementally learning latent positive and negative samples.

Imoto (2006, 2007) proposed a method to build models based on multiple predictive parameters in which independence among parameters is not necessarily assumed as it has been in previous studies.

Generalized Linear and Generalized Additive models (GLM and GAM respectively) were created using all log-transformed BLAST statistics as predictor variables ("full" models) and also using "stepwise" functions ("step" models) which iteratively build models based on the Akaike Information Criterion (AIC, see Methods).

Amongst the composition-based classification methods, while TACOA [ 7] builds models based on the ratio of observed and expected frequencies of all possible tetra- and penta-nucleotides, Phylopythia [ 8] uses Support Vector Machines to capture patterns of oligonucleotide frequency distributions observed in available genome sequences.

In this QSAR modeling workflow, a well-established method [82] is used to define the domain of applicability of the built models based on the Euclidean distances among the training data and IVS.

As such, for each species, we built models based on all combinations of n−1 data layers, where n is the total number of layers.

GARP is an evolutionary-computing method that builds models based on non-random associations between known occurrence points for species and sets of GIS coverages describing the ecological landscape.

Using multivariable linear regression, we built models based on bivariate associations and on our expectation of which variables would independently predict the outcomes as demonstrated by prior studies (8, 9, 28, 29).