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Here, data will be missing completely at random (MCAR) because the process that causes missingness does not depend on the values of variables in the dataset subject to analysis (Little, 1988; Rubin, 1976; Zhang, 2003).

The weights are chosen so that the factors iteratively explain the maximum amount of variation in the dataset subject to certain prior assumptions; these factors produce concise summaries of consistent patterns of expression for large numbers of genes.

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For each species we ran fifty submodels each trained to a randomly selected bootstrap of this dataset, subject to the constraint that each bootstrap contained a minimum of 10 occurrence and 10 background records.

Table 1 The details of the RusDS dataset Subjects Gender Min, average, max ages Healthy 250 males/250 females 18, 31.1, 79 Patient 290 males/210 females 29, 46.3, 83.

In the dataset A/B, subject no. 3 was the most difficult for attention estimation with an average accuracy of 0.55.

The dataset is subject to preprocessing methods such as normalization and feature selection and then supervised learning methods are applied to predict the importance index of the terms for certain periods.

The structures in the dataset are subject to comparison in an all-against-all approach by TS-AMIR and the results are compared with those of the CE, TM-align and 3D-BLAST programs.

In the dataset, 20 subjects' fixations are recorded for each image.

All ORFs in the dataset were subjected to orthologous protein grouping using OrthoMCL v.2.0.1 [ 69, 70].

The dataset was subjected to a Bayesian analysis and three independent analyses resulted in similar tree topologies, comparable clade probabilities and substitution model parameters.

The dataset with subjects having all nonmissing values for diabetes, the metabolic syndrome, and each component of the metabolic syndrome, is defined as the complete case dataset and is used for domain analysis.

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