Abstract
One fundamental task in molecular biology is to understand the dependency among genes or proteins to model biological networks. One widely used method is to calculate the pairwise correlation or association scores between genes or proteins. To date, a software package supporting various types of correlation measures has been lacking. In this paper, we present a pairwise correlation mining package, termed PCM, which supports the commonly used marginal correlation measures, together with two algorithms enabling the estimation of conditional correlations. Two example data sets are used to illustrate how to use this package and demonstrate the importance of having an integrated software package that incorporates various correlation measures. The package and source codes of the implementations are available at https://github.com/FeiyangGu/PCM.
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Acknowledgement
This work was partially supported by the Natural Science Foundation of China (Nos.61572094, 61502071), the Fundamental Research Funds for the Central Universities (No.DUT2017TB02) and the Science-Technology Foundation for Youth of Guizhou Province (No.KY[2017]250).
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Liang, H. et al. (2018). PCM: A Pairwise Correlation Mining Package for Biological Network Inference. In: Huang, DS., Jo, KH., Zhang, XL. (eds) Intelligent Computing Theories and Application. ICIC 2018. Lecture Notes in Computer Science(), vol 10955. Springer, Cham. https://doi.org/10.1007/978-3-319-95933-7_28
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DOI: https://doi.org/10.1007/978-3-319-95933-7_28
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