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We further developed SVM model using binary profiles of patterns as input features.

Since BalanceFix dataset consists of fixed length epitopes, therefore, we developed model using binary profile of epitopes as input features to predict antibody-specific BCEs.

The model developed using TF-IDF showed the best performance, followed by the model using word occurrence and lastly the model using binary frequency.

Although dichotomization of continuous variables may cause bias and weaken the discriminative power of the model, we used this method because a model using binary or categorical variables would be more accessible than a model using continuous variables.

The prevalence odds ratio for an ADDQoL score in the upper quartile of the distribution was estimated for the shared care and structured care models relative to the traditional mixed care model using binary logistic regression with adjustment for relevant confounders.

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The proposed model uses binary strings which represent the state of the network switches and capacitors.

The models using the ordinal outcomes did not lead to appreciably different conclusions than models using binary outcomes.

This particular situation cannot be modeled using binary interaction parameters only, in contrast to the results obtained with acetone as the precipitant.

The independence of the predictors of the MetS was tested in the multivariate models using binary logistic regression.

The correlation between metabolic syndrome and serum leptin quartiles was calculated independent of age, tobacco usage, and BMI in three models using binary logistic regression analysis (Table 4).

Additionally, we also developed SVM based models using binary profile where each position is represented by a vector of dimension of 20 (each element represent presence or absence of a specific type of residue).

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