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Figure  6 shows the n = 10 evolved input features for the coarse-scale model.

A hybrid algorithm with the combination of particle swarm optimization and gravitational search algorithm (PSOGSA) is utilized to fine-tune the optimal value of weights and bias in the ORELM network structure, while the binary version of PSOGSA (BPSOGSA) is exploited as a feature selection method to construct the most relevant input feature matrix for the model.

In this study, the input features for the classical methods included 13 diagnostic features and 82 texture features.

The input features for the subsequent analysis were the smoothed modulated normalized GM images.

For example, one could use the relative image locations of pairs of poles as input features to the model and consider independent noise on each of these input measurements.

From this exploration we identify specific features that are used as input features for our model.

Position weight matrices (PWM) for DSE and USE along with the PAS signal were used as input features for hidden Markov model (HMM) or support vector machines (SVM) (Hajarnavis et al., 2004; Legendre and Gautheret, 2003; Liu et al., 2003; Salamov and Solovyev, 1997; Tabaska and Zhang, 1999).

(ZIP 500 KB) 13321_2014_41_MOESM2_ESM.pdf Additional file 2: Investigate input features for carcinogenicity models.

Nearly all the sequence-based methods adopted PSSM as an input feature for prediction.

Input parameters for the model were estimated using primary data from longitudinal epidemiological studies and other published literature, and for some inputs, were indirectly estimated using calibration methods.

For this specific purpose, the input features of the RF model are the sequences of the antibody pairs along with other sequence-derived features described below.