Computational Analysis Of Differentially Expressed Genes In Mycobacterium Tuberculosis Infection
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Description
Tuberculosis remains a serious social and
public health problem, affecting millions of people annually,
and is reported at the end of 2014 by the World Health
01·ganization as one of the world's deadliest communicable
diseases. The most challenging being the multi-drug
resistant strains of the mycobacterium. Anothe1· maj01·
challenge fmstmting the effective control of this disease,
especially in po01· countries, is the long time taken to
diagnose it, the standa1·d diagnosis of TB is by miuoscopy,
but this does not give any inf01·mation on d1·ug-resistance -
the cell culture tests take two weeks, by which time it might
have sp1·ead to many othe1· people. In this project, the
autho1·s utilized various Statistical and Computational
techniques to analyze and discove1· genes that a1·e
diffe1·entially exp1·essed in human blood cell (Peripheml
blood mononuclea1· cells, PBMCs) subsequent to its
stimulation with heat-killed Mycobacterium on comparison
with an Roswell Pa1·k Memorial Institute (RPMI) culture
medium as a control. Using this in-silico technique, some
unique bioma1·kers we1·e discove1·ed which a1·e fm'the1·
discussed in details. These bioma1·ken identified as
diffe1·entially exp1·essed in the human blood cell will not only
enhance om· understanding of the pathogen, but is also a
spring boa1·d fo1· the completion of an Electronic hand-held,
DNA-Based Tube1-culosis diagnosis device. Om· anticipated
new technology is at the intersection of genetics and
compute1· science that will be used f01· rapid and early
detection of Mycobacterium Tuberculosis infection, a pedect
altemative to all existing symptom based diagnostic tool.
Keywords
QA75 Electronic computers. Computer science, QR Microbiology