2019
DOI: 10.1371/journal.pmed.1002786
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Host-response-based gene signatures for tuberculosis diagnosis: A systematic comparison of 16 signatures

Abstract: Background The World Health Organization (WHO) and Foundation for Innovative New Diagnostics (FIND) have published target product profiles (TPPs) calling for non-sputum-based diagnostic tests for the diagnosis of active tuberculosis (ATB) disease and for predicting the progression from latent tuberculosis infection (LTBI) to ATB. A large number of host-derived blood-based gene-expression biomarkers for diagnosis of patients with ATB have been proposed to date, but none have been implemented in cli… Show more

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Cited by 131 publications
(156 citation statements)
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References 34 publications
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“…This allowed us to perform a comprehensive, head-to-head analysis of candidate signatures for incipient TB for the first time, ensuring that each head-to-head comparison was performed on paired data. This contrasts with a recent head-to-head systematic evaluation that included only two of the eight best-performing signatures in our analysis, and compared performance for incipient TB in only one dataset over a 0-6 month time period 40 . Finally, our meta-analytic methods ensured a standardised approach to RNAseq data.…”
Section: Strengths and Weaknessesmentioning
confidence: 96%
See 1 more Smart Citation
“…This allowed us to perform a comprehensive, head-to-head analysis of candidate signatures for incipient TB for the first time, ensuring that each head-to-head comparison was performed on paired data. This contrasts with a recent head-to-head systematic evaluation that included only two of the eight best-performing signatures in our analysis, and compared performance for incipient TB in only one dataset over a 0-6 month time period 40 . Finally, our meta-analytic methods ensured a standardised approach to RNAseq data.…”
Section: Strengths and Weaknessesmentioning
confidence: 96%
“…(CD1C + BLK)); and (2) as an SVM using the four constituent gene pairs, as previously described 40 . Since the former approach achieved marginally better 18…”
Section: Author Contributionsmentioning
confidence: 99%
“…Historically, applying machine learning to diagnose acute infections using transcriptomic data has been confounded by technical and clinical heterogeneity in attempts to translate to realworld patient populations. For example, regression and decision tree classifiers trained using data collected on one type of microarray and tested in another type perform poorly, arguably at least in part due to inadequate cross-platform normalization 13,17 . Even models tested in data from the same technical platform can be prone to overfitting due to the lack of adequate representation of clinical heterogeneity in the training data 18 .…”
mentioning
confidence: 99%
“…The integration provided a comprehensive view of the differential expression profile of several genes involved in processes ranging from the innate and adaptive immune system to structural components and metabolic pathways. In addition, future datasets, whether from microarrays or RNA sequencing can also add information regarding the revealed genes and pathways and could even be aggregated to create gene expression signatures representative of leprosy [31].…”
Section: Discussionmentioning
confidence: 99%