The Eminence of Co-Expressed Ties in Schizophrenia Network Communities

Amulyashree Sridhar, Sharvani Gs, Manjunatha Reddy Ah, Biplab Bhattacharjee and Kalyan Nagaraj
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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
Manjunatha Reddy Ah: 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

Data, 2019, vol. 4, issue 4, 1-23

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”.

Keywords: schizophrenia; biological network; community detection; modularity maximization; tie structure (search for similar items in EconPapers)
JEL-codes: C8 C80 C81 C82 C83 (search for similar items in EconPapers)
Date: 2019
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