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Open AccessArticle

The Eminence of Co-Expressed Ties in Schizophrenia Network Communities

1
Department of Computer Science and Engineering, RV College of Engineering, Bangalore 560059, India
2
Department of Biotechnology, RV College of Engineering, Bangalore 560059, India
3
Department of Management, Amrita Vishwa Vidyapeetham, Amritapuri, Kollam 690525, India
*
Author to whom correspondence should be addressed.
Data 2019, 4(4), 149; https://doi.org/10.3390/data4040149
Received: 11 September 2019 / Revised: 6 November 2019 / Accepted: 27 November 2019 / Published: 29 November 2019
(This article belongs to the Special Issue Data-Driven Healthcare Tasks: Tools, Frameworks, and Techniques)
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”. View Full-Text
Keywords: schizophrenia; biological network; community detection; modularity maximization; tie structure schizophrenia; biological network; community detection; modularity maximization; tie structure
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Sridhar, A.; GS, S.; Reddy, A.M.; Bhattacharjee, B.; Nagaraj, K. The Eminence of Co-Expressed Ties in Schizophrenia Network Communities. Data 2019, 4, 149.

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