414,721 research outputs found

    Foreground detection enhancement using Pearson correlation filtering

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    Foreground detection algorithms are commonly employed as an initial module in video processing pipelines for automated surveillance. The resulting masks produced by these algorithms are usually postprocessed in order to improve their quality. In this work, a postprocessing filter based on the Pearson correlation among the pixels in a neighborhood of the pixel at hand is proposed. The flow of information among pixels is controlled by the correlation that exists among them. This way, the filtering performance is enhanced with respect to some state of the art proposals, as demonstrated with a selection of benchmark videos.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

    Improved prediction of protein interaction from microarray data using asymmetric correlation

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    Background:Detection of correlated gene expression is a fundamental process in the characterization of gene functions using microarray data. Commonly used methods such as the Pearson correlation can detect only a fraction of interactions between genes or their products. However, the performance of correlation analysis can be significantly improved either by providing additional biological information or by combining correlation with other techniques that can extract various mathematical or statistical properties of gene expression from microarray data. In this article, I will test the performance of three correlation methods-the Pearson correlation, the rank (Spearman) correlation, and the Mutual Information approach-in detection of protein-protein interactions, and I will further examine the properties of these techniques when they are used together. I will also develop a new correlation measure which can be used with other measures to improve predictive power.
 
Results:Using data from 5,896 microarray hybridizations, the three measures were obtained for 30,499 known protein-interacting pairs in the Human Protein Reference Database (HPRD). Pearson correlation showed the best sensitivity (0.305) but the three measures showed similar specificity (0.240 - 0.257). When the three measures were compared, it was found that better specificity could be obtained at a high Pearson coefficient combined with a low Spearman coefficient or Mutual Information. Using a toy model of two gene interactions, I found that such measure combinations were most likely to exist at stronger curvature. I therefore introduced a new measure, termed asymmetric correlation (AC), which directly quantifies the degree of curvature in the expression levels of two genes as a degree of asymmetry. I found that AC performed better than the other measures, particularly when high specificity was required. Moreover, a combination of AC with other measures significantly improved specificity and sensitivity, by up to 50%. 
 
Conclusions: A combination of correlation measures, particularly AC and Pearson correlation, can improve prediction of protein-protein interactions. Further studies are required to assess the biological significance of asymmetry in expression patterns of gene pairs. 
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    Appropriate Similarity Measures for Author Cocitation Analysis

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    We provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis

    GDP DILEMMA ANALYZED IN TERMS OF CORRELATION BETWEEN LISBON INDEX AND GDP PER CAPITA

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    The objective of this paper is to analyze the correlation between the index of Lisbon in 2010 and GDP per capita in 43 countries, in order to determine whether exist or not a direct and close correlation between the two indicators. The reason behind the initiation of this review is related to the current dilemma, namely whether the level of GDP reflects or not the degree of welfare of a country or region. If this is true, ie GDP provides an accurate picture of a country’s welfare level, there must be direct and strong correlation between two indicators: GDP per capita and Lisbon index. Otherwise, if the GDP is not a representative indicator of the level of welfare, the correlation should be reduced. Further analysis will show the result of that reasoning. Pearson coefficient was calculated, and it was obtained a value of 0.828 which means a strong and direct correlation between the two indicators, in a first phase. After analysis of the two clusters created can be concluded that in developing countries is a direct and strong correlation (Pearson coefficient is 0.703), while in developed countries there is direct correlation but unrepresentative (Pearson coefficient is 0.477).GDP, Lisbon Index, welfare, correlation
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