Robustness evaluations of pathway activity inference methods on gene expression data.

BACKGROUND: With the exponential growth of high-throughput technologies, multiple pathway analysis methods have been proposed to estimate pathway activities from gene expression profiles. These pathway activity inference methods can be divided into two main categories: non-Topology-Based (non-TB) and Pathway Topology-Based (PTB) methods. Although some review and survey articles discussed the topic from different aspects, there is a lack of systematic assessment and comparisons on the robustness of these approaches. RESULTS: Thus, this study presents comprehensive robustness evaluations of seven widely used pathway activity inference methods using six cancer datasets based on two assessments. The first assessment seeks to investigate the robustness of pathway activity in pathway activity inference methods, while the second assessment aims to assess the robustness of risk-active pathways and genes predicted by these methods. The mean reproducibility power and total number of identified informative pathways and genes were evaluated. Based on the first assessment, the mean reproducibility power of pathway activity inference methods generally decreased as the number of pathway selections increased. Entropy-based Directed Random Walk (e-DRW) distinctly outperformed other methods in exhibiting the greatest reproducibility power across all cancer datasets. On the other hand, the second assessment shows that no methods provide satisfactory results across datasets. CONCLUSION: However, PTB methods generally appear to perform better in producing greater reproducibility power and identifying potential cancer markers compared to non-TB methods.

An Entropy-Based Directed Random Walk for Cancer Classification Using Gene Expression Data Based on Bi-Random Walk on Two Separated Networks.

The integration of microarray technologies and machine learning methods has become popular in predicting the pathological condition of diseases and discovering risk genes. Traditional microarray analysis considers pathways as a simple gene set, treating all genes in the pathway identically while ignoring the pathway network's structure information. This study proposed an entropy-based directed random walk (e-DRW) method to infer pathway activities. Two enhancements from the conventional DRW were conducted, which are (1) to increase the coverage of human pathway information by constructing two inputting networks for pathway activity inference, and (2) to enhance the gene-weighting method in DRW by incorporating correlation coefficient values and t-test statistic scores. To test the objectives, gene expression datasets were used as input datasets while the pathway datasets were used as reference datasets to build two directed graphs. The within-dataset experiments indicated that e-DRW method demonstrated robust and superior performance in terms of classification accuracy and robustness of the predicted risk-active pathways compared to the other methods. In conclusion, the results revealed that e-DRW not only improved the prediction performance, but also effectively extracted topologically important pathways and genes that were specifically related to the corresponding cancer types.

The Malaysian Muslim Community's Understanding of So-Called Prophetic Health and Cosmetic Products.

The present work aimed to identify and describe the Malaysian Muslim community's understanding of health and cosmetic products related to the sunnah of Prophet Muhammad which are available in the Malaysian market. The demographics of this understanding are examined with respect to gender, age, marital and working status, highest level of education, and monthly income earned. A survey was conducted in 2017. A structured questionnaire pertaining to such products was used to capture the relevant data. This survey implemented a multistage design stratified by state, proportionate to the size of the state population, and was representative of the Malaysian population. Data analysis of the results was carried out using frequency and Chi-square analysis with the help of Statistical Packages for Social Science (SPSS) version 22.0. The paper concluded that the community's understanding of the term 'prophetic products' is that it refers to various products that Prophet Muhammad used and/or spoke of approvingly such as dates, raisins, pomegranates, honey, and others. It was observed that these ingredients were strongly identified in public perception as prophetic health and cosmetic products and that there is consequently great demand for these among Malaysians. This factor was identified through various elements. First, the combination of things recognized as prophetic items such as dates, raisins, pomegranates, honey, and others within the product. Second, the labeling of merchandise as prophetic products. Prophetic health merchandise was more popular among Malaysians than were cosmetic products.

Understanding the Perception of Islamic Medicine Among the Malaysian Muslim Community.

This study was conducted to identify and describe the patients' perceptions of Islamic medicine based on gender, age, marital, educational level and working status among the Malaysian Muslim population. A nationwide interviewer-administered questionnaire survey was conducted in 2013. An open-ended questionnaire pertaining to Islamic medicine was used to increase the probability of capturing maximum data. This survey implemented a multistage design, stratified by state, proportionate to the size of the state population and was representative of the Malaysian population. Post-survey classification of results was performed accordingly. Complex data analysis was carried out using SPSS 16.0. The discussion was identified and categorised into various sections. The paper concludes that Islamic medicine has a major influence in the Malaysian Muslim community compared to other alternatives. Further, its potential for growth and importance especially for treating spiritual ailments cannot be denied. The respondents indicated that two factors motivate Islamic medicine in Malaysia: (1) the Muslim community opts for alternative healing because of their dissatisfaction with conventional methods; (2) Islamic medicine focuses only on healing spiritual-related problems. The average perception of respondents is that the function of Islamic medicine in healing physical diseases is undervalued and that it is not suitable to replace the functions of modern health institutions.

An enhanced topologically significant directed random walk in cancer classification using gene expression datasets.

Microarray technology has become one of the elementary tools for researchers to study the genome of organisms. As the complexity and heterogeneity of cancer is being increasingly appreciated through genomic analysis, cancerous classification is an emerging important trend. Significant directed random walk is proposed as one of the cancerous classification approach which have higher sensitivity of risk gene prediction and higher accuracy of cancer classification. In this paper, the methodology and material used for the experiment are presented. Tuning parameter selection method and weight as parameter are applied in proposed approach. Gene expression dataset is used as the input datasets while pathway dataset is used to build a directed graph, as reference datasets, to complete the bias process in random walk approach. In addition, we demonstrate that our approach can improve sensitive predictions with higher accuracy and biological meaningful classification result. Comparison result takes place between significant directed random walk and directed random walk to show the improvement in term of sensitivity of prediction and accuracy of cancer classification.

Profiling deleterious non-synonymous SNPs of smoker's gene CYP1A1.

CYP1A1 gene belongs to the cytochrome P450 family and is known better as smokers' gene due to its hyperactivation as a consequence of long term smoking. The expression of CYP1A1 induces polycyclic aromatic hydrocarbon production in the lungs, which when over expressed, is known to cause smoking related diseases, such as cardiovascular pathologies, cancer, and diabetes. Single nucleotide polymorphisms (SNPs) are the simplest form of genetic variations that occur at a higher frequency, and are denoted as synonymous and non-synonymous SNPs on the basis of their effects on the amino acids. This study adopts a systematic in silico approach to predict the deleterious SNPs that are associated with disease conditions. It is inferred that four SNPs are highly deleterious, among which the SNP with rs17861094 is commonly predicted to be harmful by all tools. Hydrophobic (isoleucine) to hydrophilic (serine) amino acid variation was observed in the candidate gene. Hence, this investigation aims to characterize a candidate gene from 159 SNPs of CYP1A1.