An integrated framework modelling susceptibility to tuberculosis in homogeneous and admixed populations
Bibliographic record
Abstract
In spite of the wide variety of anti-tuberculosis drugs, tuberculosis (TB), caused by mycobacterium tuberculosis (MTB), is the second leading infectious disease after Human Immunodeciency Virus (HIV) or Acquired Immunodeciency Syndrome (AIDS), and one of the leading causes of human death from infectious diseases, especially in Sub-Saharan Africa.Approximately onethird of the world population are latently infected with MTB, of which, 10 % progress to active TB.Obstacles in TB control include lengthy treatment regimens of more than 6 months, drug resistance, lack of an eective vaccine and limited knowledge and incomplete information about factors that trigger the progression of an MTB infection to disease.Moreover, the association of TB and HIV or AIDS has also promoted all of the conditions of an explosive increase in TB incidence and prevalence.Several studies suggest that host genetic factors also aect susceptibility and resistance to TB. Genome wide association study (GWAS) provides a way of examining many common variants in dierent populations to see if any variant is associated with a trait by searching for small variations, called single nucleotide polymorphisms (SNPs).However, it is well known that GWAS alone is insucient to elucidate the genetic structure of a complex disease and may lead to non conclusive results.In this thesis, we use a post association analysis, which has been suggested as a new paradigm to GWAS, to elucidate and analyze human genetic susceptibility in relation to the infecting MTB by combining association signals from GWAS and available functional and comparative genomics information for human and MTB.We have identied 6 disease associated genes for the admixed ii
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".