Background: With the availability of large-scale genome-wide association study (GWAS) data, choosing an optimal set of SNPs for disease susceptibility prediction is a challenging task. This study aimed to use single n...
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Background: With the availability of large-scale genome-wide association study (GWAS) data, choosing an optimal set of SNPs for disease susceptibility prediction is a challenging task. This study aimed to use single nucleotide polymorphisms (SNPs) to predict psoriasis from searching GWAS data. Methods: Totally we had 2,798 samples and 451,724 SNPs. Process for searching a set of SNPs to predict susceptibility for psoriasis consisted of two steps. The first one was to search top 1,000 SNPs with high accuracy for prediction of psoriasis from GWAS dataset. The second one was to search for an optimal SNP subset for predicting psoriasis. The sequential information bottleneck (sIB) method was compared with classical linear discriminant analysis(LDA) for classification performance. Results: The best test harmonic mean of sensitivity and specificity for predicting psoriasis by sIB was 0.674(95% CI: 0.650-0.698), while only 0.520(95% CI: 0.472-0.524) was reported for predicting disease by LDA. Our results indicate that the new classifier sIB performs better than LDA in the study. Conclusions: The fact that a small set of SNPs can predict disease status with average accuracy of 68% makes it possible to use SNP data for psoriasis prediction.
To reduce the negative effects of tourism on the environment, the importance of ecotourism is increasingly considered because this form of tourism helps to protect the environment and sustainable development of an are...
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To reduce the negative effects of tourism on the environment, the importance of ecotourism is increasingly considered because this form of tourism helps to protect the environment and sustainable development of an area. So, it is important to determine suitable places for tourism to better manage the study area. The aim of this study is to identify potential ecotourism sites using ordered weight averaging (OWA) and fuzzy quantifier algorithms in the east and central of Fars province, Iran. Required spatial data such as geology, soil, slope land, topographic roughness index (TRI), vegetation, surface water, elevation, protected area, climate, distance to road, and distance to the village were utilized. To prepare ecotourism maps with different confidence levels, eleven ordered weights were applied corresponding to the eleven parameters that were rank-ordered for each parameter after the modified factor weights were applied. Also, the feature selection algorithm (random search and genetic search methods) was used to select the most important parameters to determine the ecotourism map. The results showed that, with decreasing risk (alpha = 0), almost all of the study area was unsuitable for ecotourism while, with increasing risk (alpha = 20), all of the study areas were suitable for ecotourism. One of the ecotourism maps prepared with different confidence levels can be suggested based on the different conditions of tourists so that, if the tourist has a limited time, ecotourism maps with a higher degree of confidence levels are recommended and vice versa. This is one of the innovations of the present research. Also, the results of the random search method with the least error show that slope, elevation, climate, distance to river, and distance to road parameters are the most important parameters in preparing the ecotourism map of the region. So, using the results of the research, many economic problems, such as unemployment, will be solved by managers by preparing tour
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