Department of Bioinformatics , Sathyabama University, India
Corresponding author details:
Harishchander Anandaram
Department of Bioinformatics
Sathyabama University
India
Copyright:
© 2018 Anandaram H. This is
an open-access article distributed under the
terms of the Creative Commons Attribution 4.0
international License, which permits unrestricted
use, distribution and reproduction in any
medium, provided the original author and source
are credited.
In the era of post genomics, performing a computational analysis to understand the
pharmacogenomic based regulation of Psoriasis with respect to the principles of data
mining and constructing a regulatory network with respect to the principles of systems
biology and analyzing the network with respect to the principles of test statistic remains
a challenging task to execute. The challenge was approached by identifying the associated
genes of Psoriasis from PharmGkb and it was followed by identifying the associated
regulators (MicroRNAs and Transcription Factors) from PharmacomiR/RegNetworks.
Finally the regulatory networks were analyzed by the statistical measures.
Overall network analysis of pharmacogenomic based regulatory
network in psoriasis resulted in identifying 25 potential regulators of Psoriasis [20 Transcription Factors (VDR, MTHFR, GSTP1, ABCC1,
TYMS, SLC19A1, CYP1A2, HLA-B, MAX, MYC, AHR, ARNT, CUX1, E2F1
and EP300) and 5 miRNAs (hsa-miR-103, hsa-miR-107, hsa-miR125a-3p, hsa-miR-138 and hsa-miR-24)]. In a biological context,
these potential regulators of psoriasis have a maximum probability to
become a potential biomarker for Psoriasis and there was an identical
pattern in the comparative-network analysis to illustrate the fact that
there is a maximum probability for these potential regulators to be
considered to treat psoriasis in future
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