In Section 7, we present the dataset used for learning and tests and summarize the scores of validation tests of the proposed approach.
To avoid bias, the scoring of validation samples using the 20 gene signature was done without any scaling or tuning.
With the total number of 100 positive and negative sequences stated above, we computed Z-scores for log-odds scores of validation sequences in each HMM model, and drew receiver operating characteristic (ROC) curves and calculate area under the ROC curve (AUC) to judge the discriminative power.
After the calculation, a table is created to display the classifier, parameter, best parameter, number of positive samples, number of negative samples and score_means (the mean score of cross-validation for each parameter combination).
Table 4 presents the Brier scores of the validation samples for each country and for the nine models.
The mean PANSS scores of the Validation subsample for the 19-item PANSS are as follows, Positive subscale 17.52 (SD = 6.08) (6 to 36 score range), Negative subscale 19.01 (SD = 6.17) (6 to 39 score range), General Psychopathology subscale, 17.54 (SD = 4.85) (7 to 33 score range), and total PANSS 54.07 (SD = 13.27) (19 to 98 score range).
All CNVs were subsequently required to pass a median z-score outlier method of validation that helps to remove false-positive CNVs, and to identify any missed CNVs.
Among the 41 trSNPs, validation scores of 33 variations (80.5%) were less than 3, while among the 30 snSNPs, validation scores of 23 variations (76.7%) were greater than or equal to 3. The percent of variations of validation score greater than 9 was 7.32% (3/41) for the trSNPs and 35.5% (11/31) for the snSNPs.
The number of questions/items of each QAT, information on the structure (checklist, scale, summary score) and validation of each QAT was summarized across all QATs.
Specifically, survey design reflects categories of "assessment program design," "assessment instruments and scoring systems," and "processes of validation".
