This article discusses issues of scales in measuring attitude, demonstrates how a metric scale can be generated based on three main features, and presents results from a repeated measurement survey to verify the generated scale. The design of the generated metric scale is introduced and named Ruler and Option (RO). The population for repeated measurement survey was 1,870 bachelor students from a public university. Two sets of questionnaire (identical items), one with 7-point Likert scale and another with RO scale, were distributed to a sample of 595 bachelor students chosen using stratified random sampling method. Data were analyzed descriptively using SPSS version 20 and structural equation modeling using AMOS version 21. Results showed that data from RO scale performed better than data from 7-point Likert scale in terms of number of items per construct, factor loadings, squared multiple correlations, higher ratio of degrees of freedom to number of parameters, and higher reliability coefficients. In terms of validity coefficients, measurement models from both data sets attained almost the same level of discriminant and construct validity. Further studies are recommended to elicit the strength and weakness of RO scale to identify the situations where it is most suitable.

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BackgroundWith a higher throughput and lower cost in sequencing, second generation sequencing technology has immense potential for translation into clinical practice and in the realization of pharmacogenomics based patient care. The systematic analysis of whole genome sequences to assess patient to patient variability in pharmacokinetics and pharmacodynamics responses towards drugs would be the next step in future medicine in line with the vision of personalizing medicine.MethodsGenomic DNA obtained from a 55 years old, self-declared healthy, anonymous male of Malay descent was sequenced. The subject's mother died of lung cancer and the father had a history of schizophrenia and deceased at the age of 65 years old. A systematic, intuitive computational workflow/pipeline integrating custom algorithm in tandem with large datasets of variant annotations and gene functions for genetic variations with pharmacogenomics impact was developed. A comprehensive pathway map of drug transport, metabolism and action was used as a template to map non-synonymous variations with potential functional consequences.Principal FindingsOver 3 million known variations and 100,898 novel variations in the Malay genome were identified. Further in-depth pharmacogenetics analysis revealed a total of 607 unique variants in 563 proteins, with the eventual identification of 4 drug transport genes, 2 drug metabolizing enzyme genes and 33 target genes harboring deleterious SNVs involved in pharmacological pathways, which could have a potential role in clinical settings.ConclusionsThe current study successfully unravels the potential of personal genome sequencing in understanding the functionally relevant variations with potential influence on drug transport, metabolism and differential therapeutic outcomes. These will be essential for realizing personalized medicine through the use of comprehensive computational pipeline for systematic data mining and analysis.

<span>The curse of dimensionality and the empty space phenomenon emerged as a critical problem in text classification. One way of dealing with this problem is applying a feature selection technique before performing a classification model. This technique helps to reduce the time complexity and sometimes increase the classification accuracy. This study introduces a feature selection technique using K-Means clustering to overcome the weaknesses of traditional feature selection technique such as principal component analysis (PCA) that require a lot of time to transform all the inputs data. This proposed technique decides on features to retain based on the significance value of each feature in a cluster. This study found that k-means clustering helps to increase the efficiency of KNN model for a large data set while KNN model without feature selection technique is suitable for a small data set. A comparison between K-Means clustering and PCA as a feature selection technique shows that proposed technique is better than PCA especially in term of computation time. Hence, k-means clustering is found to be helpful in reducing the data dimensionality with less time complexity compared to PCA without affecting the accuracy of KNN model for a high frequency data.</span>