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Cites:

Information Journal Paper

Title

Inferring Co-Expression Networks from their Associated Attrib-utes by Neural Networks

Pages

  545-555

Abstract

 Gene Expression, flow of information from DNA to proteins, is a fundamental biological process. Expression of one gene can be regulated by the product of another gene. These regulatory relationships are usually modeled as a network; genes are modeled as nodes and their relationships are shown as edges. There are many efforts for discovering how genes regulate expression of themselves. This paper presents a new method that employs expression data and ontological data to infer co-expression networks, networks made by connecting genes with similar expression patterns. In brief, the method begins by learning associations between the available ontological information and the provided co-expression data. Later, the method is able to find both known and novel co-expressed pairs of genes. Finally, the method uses a self-organizing map to adjust estimation made by the previous step and to form the GCN for the input genes. The results show that the proposed method works well on the biological data and its predictions are accurate; consequently, co-expression networks generated by the proposed method are very similar to the biological networks or those that constructed with no missing data. The method is written in C++ language and is available upon request from the corresponding author.

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    • Cite

    APA: Copy

    MAHDEVAR, GH.. (2019). Inferring Co-Expression Networks from their Associated Attrib-utes by Neural Networks. JOURNAL OF BIOTECHNOLOGY, 10(4 ), 545-555. SID. https://sid.ir/paper/231254/en

    Vancouver: Copy

    MAHDEVAR GH.. Inferring Co-Expression Networks from their Associated Attrib-utes by Neural Networks. JOURNAL OF BIOTECHNOLOGY[Internet]. 2019;10(4 ):545-555. Available from: https://sid.ir/paper/231254/en

    IEEE: Copy

    GH. MAHDEVAR, “Inferring Co-Expression Networks from their Associated Attrib-utes by Neural Networks,” JOURNAL OF BIOTECHNOLOGY, vol. 10, no. 4 , pp. 545–555, 2019, [Online]. Available: https://sid.ir/paper/231254/en

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