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The Eminence of Co-Expressed Ties in Schizophrenia Network Communities

Author

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  • Amulyashree Sridhar

    (Department of Computer Science and Engineering, RV College of Engineering, Bangalore 560059, India)

  • Sharvani GS

    (Department of Computer Science and Engineering, RV College of Engineering, Bangalore 560059, India)

  • AH Manjunatha Reddy

    (Department of Biotechnology, RV College of Engineering, Bangalore 560059, India)

  • Biplab Bhattacharjee

    (Department of Management, Amrita Vishwa Vidyapeetham, Amritapuri, Kollam 690525, India)

  • Kalyan Nagaraj

    (Department of Computer Science and Engineering, RV College of Engineering, Bangalore 560059, India)

Abstract

Exploring gene networks is crucial for identifying significant biological interactions occurring in a disease condition. These interactions can be acknowledged by modeling the tie structure of networks. Such tie orientations are often detected within embedded community structures. However, most of the prevailing community detection modules are intended to capture information from nodes and its attributes, usually ignoring the ties. In this study, a modularity maximization algorithm is proposed based on nonlinear representation of local tangent space alignment (LTSA). Initially, the tangent coordinates are computed locally to identify k -nearest neighbors across the genes. These local neighbors are further optimized by generating a nonlinear network embedding function for detecting gene communities based on eigenvector decomposition. Experimental results suggest that this algorithm detects gene modules with a better modularity index of 0.9256, compared to other traditional community detection algorithms. Furthermore, co-expressed genes across these communities are identified by discovering the characteristic tie structures. These detected ties are known to have substantial biological influence in the progression of schizophrenia, thereby signifying the influence of tie patterns in biological networks. This technique can be extended logically on other diseases networks for detecting substantial gene “hotspots”.

Suggested Citation

  • Amulyashree Sridhar & Sharvani GS & AH Manjunatha Reddy & Biplab Bhattacharjee & Kalyan Nagaraj, 2019. "The Eminence of Co-Expressed Ties in Schizophrenia Network Communities," Data, MDPI, vol. 4(4), pages 1-23, November.
  • Handle: RePEc:gam:jdataj:v:4:y:2019:i:4:p:149-:d:292320
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    References listed on IDEAS

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