mca 16(10): e3

Research Article

Abundance of connected motifs in transcriptional networks, a case study using random forests regression

Download1028 downloads
  • @ARTICLE{10.4108/eai.3-12-2015.2262520,
        author={Syed Khajamoinuddin and Bhanu Kamapantula and Michael Mayo and Edward Perkins and Preetam Ghosh},
        title={Abundance of connected motifs in transcriptional  networks, a case study using random forests regression},
        journal={EAI Endorsed Transactions on Mobile Communications and Applications},
        volume={3},
        number={10},
        publisher={ACM},
        journal_a={MCA},
        year={2017},
        month={6},
        keywords={motif connectivity, transcriptional networks, complex networks, vertex-shared motifs, connected motifs},
        doi={10.4108/eai.3-12-2015.2262520}
    }
    
  • Syed Khajamoinuddin
    Bhanu Kamapantula
    Michael Mayo
    Edward Perkins
    Preetam Ghosh
    Year: 2017
    Abundance of connected motifs in transcriptional networks, a case study using random forests regression
    MCA
    EAI
    DOI: 10.4108/eai.3-12-2015.2262520
Syed Khajamoinuddin1, Bhanu Kamapantula1, Michael Mayo2, Edward Perkins2, Preetam Ghosh,*
  • 1: Virginia Commonwealth University
  • 2: US Army ERDC
*Contact email: pghosh@vcu.edu

Abstract

Biological network topologies are known to be robust despite internal and external perturbances. Motifs such as feed-forward loop and bifan have been marked to contribute to structural and functional significance. While network characteristics such as network density, average shortest path, and centrality measures etc., have been well studied, modular characteristics have not been explored in similar detail. Motif connectivity might play a major role in regulation under high perturbations. Connected motif abundance can skew network robustness as well. To test this hypothesis, we study the significance of the two connected feed-forward loop motifs using random forest regression modeling. We define thirty eight network features, fifteen of which are static and dynamic features and the other twenty three are two feed-forward loop connected motif features. We identify significant features among these using random forests regression and create models that can be used to train and predict the robustness of the biological networks. The performance of these models is measured using coefficient of determination metric and the significance of the features themselves is characterized using feature importance. Our experiments reveal that connected feed-forward loop motifs do not contribute to the robustness of network when models are created with all 38 features. For models with only connected motif features, the performance of a specific rhombus motif under high loss stands out.