Database normalization

Abstract: Gene expression analysis of microRNA molecules is becoming increasingly important. In this study we assess the use of the mean expression value of all expressed microRNAs in a given sample as a normalization factor for microRNA real-time quantitative PCR data and compare its performance to the currently adopted approach. We demonstrate that the mean expression value outperforms the current normalization strategy in terms of better reduction of technical variation and more accurate appreciation of biological changes.

Abstract: Recent advances in artificial intelligence have allowed the application of such technologies in real industrial problems. We have studied the application of backpropagation neural networks to several problems of estimation and identification in nuclear power plants. These problems often have been reported to be very time-consuming in the training phase. Among the different approaches suggested to ease the backpropagation training process, input data pretreatment has been pointed out, although no specific procedure has been proposed. We have found that input data normalization with certain criteria, prior to a training process, is crucial to obtain good results as well as to fasten significantly the calculations. This paper shows how data normalization affects the performance error of parameter estimators trained to predict the value of several variables of a PWR nuclear power plant. The criteria needed to accomplish such data normalization are also described.

Abstract: Non-targeted metabolomics based on mass spectrometry enables high-throughput profiling of the metabolites in a biological sample. The large amount of data generated from mass spectrometry requires intensive computational processing for annotation of mass spectra and identification of metabolites. Computational analysis tools that are fully integrated with multiple functions and are easily operated by users who lack extensive knowledge in programing are needed in this research field. We herein developed an R package, metaX, that is capable of end-to-end metabolomics data analysis through a set of interchangeable modules. Specifically, metaX provides several functions, such as peak picking and annotation, data quality assessment, missing value imputation, data normalization, univariate and multivariate statistics, power analysis and sample size estimation, receiver operating characteristic analysis, biomarker selection, pathway annotation, correlation network analysis, and metabolite identification. In addition, metaX offers a web-based interface ( http://metax.genomics.cn ) for data quality assessment and normalization method evaluation, and it generates an HTML-based report with a visualized interface. The metaX utilities were demonstrated with a published metabolomics dataset on a large scale. The software is available for operation as either a web-based graphical user interface (GUI) or in the form of command line functions. The package and the example reports are available at http://metax.genomics.cn/ . The pipeline of metaX is platform-independent and is easy to use for analysis of metabolomics data generated from mass spectrometry.