Limited resources of genome sequencing in developing countries: Challenges and solutions
Bibliographic record
Abstract
The differences between countries in national income, growth, human development and many other factors are used to classify countries into developed and developing countries. There are several classification systems that use different sets of measures and criteria. The most common classifications are the United Nations (UN) and the World Bank (WB) systems. The UN classification system uses the UN Human Development Index (HDI), an indicator that uses statistic of life expectancy, education, and income per capita for countries' classification. While the WB system uses gross national income (GNI) per capita that is calculated using the World Bank Atlas method. According to the UN and WB classification systems, there are 151 and 134 developing countries, respectively, with 89% overlap between the two systems. Developing countries have limited human development, and limited expenditure in education and research, among several other limitations. The biggest challenge facing genomic researchers and clinicians is limited resources. As a result, genomic tools, specifically genome sequencing technologies, which are rapidly becoming indispensable, are not widely available. In this report, we explore the current status of sequencing technologies in developing countries, describe the associated challenges and emphasize potential solutions.
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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 | 0.026 | 0.058 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.014 | 0.004 |
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".