Sentence examples for model using features from inspiring English sources

Sutton et al [39] developed a predictive model using features extracted from MRI that could distinguish between invasive ductal carcinoma molecular subtypes.

Our model uses features from thermodynamic, compositional, statistical and other properties of nucleotides and polynucleotide sequences.

To encapsulate these complex patterns of residue conservation and position-specific propensities for computational prediction, we constructed SVM prediction models using features extracted using the Bayes Feature Extraction (BFE) approach recently introduced by Shao et al.[ 19].

Further, we developed models using features like composition-transition and distribution (CTD) as input, which has been used in the past for predicting BCEs [ 33] and achieved MCC of 0.29, 0.38 and 0.40 for IgG, IgE and IgA respectively (Table  1 and Additional file 3: Table S2).

The models used feature less than 1% error when compared to electromagnetic simulations while reducing the simulation time by several orders of magnitude.

Linear models use feature vectors for predictions.

We considered four methods: 1) window-specific and simultaneously adjusted regression; 2) multiple informant models; 3) using features of individual exposure patterns to predict outcomes; and 4) models of population exposure patterns depending on the outcome.

The human model is created using features of the parts in each human region.

The suitability of modelling hybrid structures using features available within commercial finite element programs and standard material properties was assessed.

On DCASE2016 dataset, only DNN model using CQT features performs better than our baseline models.

An accuracy of 71%% is achieved by BPNN model using TPL features.