DocumentCode
2513978
Title
Determination of Major Lineages of Mycobacterium tuberculosis Complex Using Mycobacterial Interspersed Repetitive Units
Author
Aminian, Minoo ; Shabbeer, Amina ; Bennett, Kristin P.
fYear
2009
fDate
1-4 Nov. 2009
Firstpage
338
Lastpage
343
Abstract
Abstract-We present a novel Bayesian network (BN) to classify strains of Mycobacterium tuberculosis complex (MTBC) into six major genetic lineages using mycobacterial interspersed repetitive units (MIRUs), a high-throughput biomarker. MTBC is the causative agent of tuberculosis (TB), which remains one of the leading causes of disease and morbidity world-wide. DNA fingerprinting methods such as MIRU are key components of modern TB control and tracking. The BN achieves high accuracy on four large MTBC genotype collections consisting of over 4700 distinct 12-loci MIRU genotypes. The BN captures distinct MIRU signatures associated with each lineage, explaining the excellent performance of the BN. The errors in the BN support the need for additional biomarkers such as the expanded 24-loci MIRU used in CDC genotyping labs since May 2009. The conditional independence assumption of each locus given the lineage makes the BN easily extensible to additional MIRU loci and other biomarkers.
Keywords
belief networks; cellular biophysics; diseases; fingerprint identification; genetics; microorganisms; molecular biophysics; patient monitoring; Bayesian network; DNA fingerprinting methods; MIRU genotypes; Mycobacterium tuberculosis complex; disease; high-throughput biomarker; major genetic lineages; mycobacterial interspersed repetitive units; tuberculosis control; tuberculosis tracking; Bayesian methods; Biomarkers; Capacitive sensors; Content addressable storage; DNA; Databases; Diseases; Fingerprint recognition; Genetics; Immune system; Bayesian network; MIRU-VNTR; lineages; tuberculosis;
fLanguage
English
Publisher
ieee
Conference_Titel
Bioinformatics and Biomedicine, 2009. BIBM '09. IEEE International Conference on
Conference_Location
Washington, DC
Print_ISBN
978-0-7695-3885-3
Type
conf
DOI
10.1109/BIBM.2009.86
Filename
5341764
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