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Data on optimal operation of Safarud Reservoir using symbiotic organisms search (SOS) algorithm.

ABSTRACT: This data article explains the time-series data for optimal operation of Safarud Reservoir located in Halilrood basin in the south of Iran for a period of 223 months, from October 2000 to April 2019. The utilized data included the release of the reservoir, reservoir inflow, reservoir storage, evaporation and precipitation. A model based on Symbiotic Organisms Search (SOS) algorithm was also developed for the optimal operation of Safarud Reservoir. The analysis of the objective function showed that the best solution achieved by the SOS algorithm was 10.89. Also, the analysis of these datasets revealed that the SOS algorithm was efficient for the optimal operation of the reservoir problem.

SUBMITTER: Rezaei-Estakhroueiyeh A

PROVIDER: S-EPMC7078506 | biostudies-literature | 2020 Apr

REPOSITORIES: biostudies-literature

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Data on optimal operation of Safarud Reservoir using symbiotic organisms search (SOS) algorithm.

Rezaei-Estakhroueiyeh Aliakbar A Jalalkamali Navid N Momeniroghabadi Mehdi M

Data in brief 20200226

This data article explains the time-series data for optimal operation of Safarud Reservoir located in Halilrood basin in the south of Iran for a period of 223 months, from October 2000 to April 2019. The utilized data included the release of the reservoir, reservoir inflow, reservoir storage, evaporation and precipitation. A model based on Symbiotic Organisms Search (SOS) algorithm was also developed for the optimal operation of Safarud Reservoir. The analysis of the objective function showed th ...[more]

PMID: 32195294

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Project description:BackgroundGene signatures are important to represent the molecular changes in the disease genomes or the cells in specific conditions, and have been often used to separate samples into different groups for better research or clinical treatment. While many methods and applications have been available in literature, there still lack powerful ones that can take account of the complex data and detect the most informative signatures.MethodsIn this article, we present a new framework for identifying gene signatures using Pareto-optimal cluster size identification for RNA-seq data. We first performed pre-filtering steps and normalization, then utilized the empirical Bayes test in Limma package to identify the differentially expressed genes (DEGs). Next, we used a multi-objective optimization technique, "Multi-objective optimization for collecting cluster alternatives" (MOCCA in R package) on these DEGs to find Pareto-optimal cluster size, and then applied k-means clustering to the RNA-seq data based on the optimal cluster size. The best cluster was obtained through computing the average Spearman's Correlation Score among all the genes in pair-wise manner belonging to the module. The best cluster is treated as the signature for the respective disease or cellular condition.ResultsWe applied our framework to a cervical cancer RNA-seq dataset, which included 253 squamous cell carcinoma (SCC) samples and 22 adenocarcinoma (ADENO) samples. We identified a total of 582 DEGs by Limma analysis of SCC versus ADENO samples. Among them, 260 are up-regulated genes and 322 are down-regulated genes. Using MOCCA, we obtained seven Pareto-optimal clusters. The best cluster has a total of 35 DEGs consisting of all-upregulated genes. For validation, we ran PAMR (prediction analysis for microarrays) classifier on the selected best cluster, and assessed the classification performance. Our evaluation, measured by sensitivity, specificity, precision, and accuracy, showed high confidence.ConclusionsOur framework identified a multi-objective based cluster that is treated as a signature that can classify the disease and control group of samples with higher classification performance (accuracy 0.935) for the corresponding disease. Our method is useful to find signature for any RNA-seq or microarray data.

Dataset Information

Data on optimal operation of Safarud Reservoir using symbiotic organisms search (SOS) algorithm.

Publications

Data on optimal operation of Safarud Reservoir using symbiotic organisms search (SOS) algorithm.

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