Purpose This study aims to investigate the psychometric properties of the social communication questionnaire (SCQ) in Iran by identifying children with autism spectrum disorder (ASD) who had Persian-speaking parents. Design/methodology/approach A case–control study was performed from June to August 2021 in Hamadan, Iran. The case group consisted of children who were examined by clinicians who used a coding scheme based on the DSM-V criteria for ASD by a psychiatrist. The control group consisted of non-ASD children who were asked to participate in the study. This study conducted the reliability, content and face validity to evaluate the psychometric properties of the tool. In the first step, Kaiser–Meyer–Olkin and Bartlett’s test were used to determine sampling adequacy and appropriateness of correlation matrix. In the second step, the exploratory factor analysis approach was used. The method of extracting the factors is done by using the varimax rotation method and selecting the number of factors using an eigenvalue and scree plot. Statistical analysis was performed using Software Package for Social Sciences 21 with the statistical significance set at level less than 0.05. Findings The quantitative content validity analysis revealed that the mean of content validity ratio and content validity index were 0.92 and 0.91, respectively. Mean score ± standard deviation in the two groups of ASD and control were 14.23 ± 3.84 and 7.83 ± 4.80, respectively. With cut-off point >12.5, sensitivity, specificity and misclassification error values were 73.33%, 80.0% and 23%, respectively. Research limitations/implications The results showed that the internal consistency of the SCQ is desirable. Also, the internal consistency of its five subscales was obtained between 0.700 and 0.87. The findings showed that SCQ questionnaire is highly reliable in reciprocal social interaction (Factor 1) and the total score, while other factors were relatively reliable. Originality/value To the best of the authors’ knowledge, this is the first paper on psychometric properties of SCQ in ASD children in Iran.
Researchers in biological sciences and genetics are faced with high-dimensional data, such as the microarray data, and the analysis and proper interpretation of these data are very important in bioinformatics and systems biological sciences. In such types of data, the number of variables, for example, the genes, is many times greater than the number of samples. Therefore, the dimension of the data must be reduced at the primary point. Then, the analysis, for example, clustering, is performed on the compacted data. This process is called data summarization. There are various ways to summarize high-dimensional data, which depends on the nature of the data. The aim of data summarization is to remove unnecessary features so that the data are classified more accurately. Shannon' s entropy information is a common method for clustering genes in microarray data and selecting a set of disease-related genes. This chapter introduces and illustrates statistical inference concepts of entropy in microarray data clustering to select a set of the most important genes associated with a disease.
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BackgroundImputation is one of the strategies for dealing with Missing values (MVs) in microarray data. Employing the best subset of genes for imputation is very important. In this study, we used mutual information gene selection before imputation to select the best subset of genes for imputation MVs and then classified imputed data. Two datasets were used, and we generate MVs with missing rates from 1 to 7 percent. K nearest neighbor, row mean imputation, and the method contains Feature Selection with Missing data by Mutual Information (FSM-MI) were employed. We used Root Mean Square Error (RMSE) for evaluating the performance of the methods. We classified complete and imputed data by random forest classifier and compare them by accuracy.ResultsFSM-MI imputation method with 0.0364 and 0.0083 mean RMSE value in GSE510 and GSE1063 datasets had the best performance, respectively. The classification accuracy of complete and imputed data in GSE510 and GSE1063 datasets were 100 and 80, and 100, 100, 83.3, and 66.7, and 80, 80, 70, and 70 percent in missing rates 1, 3, 5, and 7, respectively.ConclusionFeature selection before imputation MVs is as important as the selection of the best imputation method in improving the result of subsequent analyzes.
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