
pmid: 25342061
pmc: PMC4292082
Accurate and rapid diagnosis of active tuberculosis (TB) disease is still hampered by inadequate tools. Although current assays relying on single-marker readouts mostly display inadequate sensitivity and/or specificity, host-related multimarker signatures are especially poorly developed. As a consequence, research programs have been initiated to search for combinations of markers-so-called biosignatures with superior performance. Many such investigations harness high-throughput platforms to analyze the host response during infection and disease. A major challenge for these activities is the analysis of vast amounts of data produced. Specialized bioinformatic tools are being applied to identify the most robust biosignatures for classification of exposed and diseased individuals and prognosis of risk of disease in endemic areas. Validation of the most promising biosignatures in ongoing multicohort studies will bring us a step closer to the identification of an accurate unified signature.
Genetic Markers, Artificial Intelligence, Predictive Value of Tests, Gene Expression Profiling, Humans, Tuberculosis, Biological Assay, Sensitivity and Specificity, Algorithms, Biomarkers
Genetic Markers, Artificial Intelligence, Predictive Value of Tests, Gene Expression Profiling, Humans, Tuberculosis, Biological Assay, Sensitivity and Specificity, Algorithms, Biomarkers
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