PCM: A Pairwise Correlation Mining Package for Biological Network Inference
Autor: | Zengyou He, Chaohua Sheng, Qiong Duan, Feiyang Gu, Bo Tian, Bo Xu, Jun Wu, Hao Liang |
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Rok vydání: | 2018 |
Předmět: |
0301 basic medicine
Source code Dependency (UML) Computer science media_common.quotation_subject Association (object-oriented programming) 0206 medical engineering Correlation and dependence Biological network inference 02 engineering and technology computer.software_genre Data set 03 medical and health sciences Task (computing) 030104 developmental biology Data mining computer 020602 bioinformatics Biological network media_common |
Zdroj: | Intelligent Computing Theories and Application ISBN: 9783319959320 ICIC (2) |
DOI: | 10.1007/978-3-319-95933-7_28 |
Popis: | 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. |
Databáze: | OpenAIRE |
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