Title: Graph pruning based approach for inferring disease causing genes and associated pathways

Authors: Jeethu V. Devasia; Priya Chandran

Addresses: Department of Computer Science and Engineering, National Institute of Technology Calicut, Calicut, 673601, India ' Department of Computer Science and Engineering, National Institute of Technology Calicut, Calicut, 673601, India

Abstract: The problem of inferring disease causing genes and dysregulated pathways has obtained a vital position in computational biology research. But, the huge size of the biological network makes this process computationally challenging. Here, we tackle the problem of inferring disease causing genes and associated pathways using graph pruning techniques which focus on the improvement in accuracy of results in reasonable execution time and fetching more causal genes and their pathways. Experimentation of the proposed approach and the reported approaches in literature was done on real biological data. More efficient results in terms of accuracy and execution time based on benchmark datasets were obtained as its outcome. If the function of the newly identified genes/pathways in the disease states could be validated biologically, for any unknown influences in the disease development, it would significantly affect our effort to design new drug targets and defeat the diseases.

Keywords: biological network; gene expression; disease causing genes; dysregulated pathways; graph pruning.

DOI: 10.1504/IJBRA.2019.103789

International Journal of Bioinformatics Research and Applications, 2019 Vol.15 No.4, pp.359 - 370

Accepted: 08 Oct 2017
Published online: 25 Nov 2019 *

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