PERSONALIZING MEDICINE: ANALYZING NEXT GENERATION SEQUENCING USING A SYMPTOMS-BASED APPROACH ON PUBMED
Bibliographic record
Abstract
Personalized medicine is the future of healthcare. Let us assume that there is a child with a rare genetic illness that results in cardiac arrest during teenage years. To best diagnose and treat this illness, doctors and scientists have to figure out which one of his 25,000 genes is defective. This modern version of finding a needle in a haystack is costly when patients are suffering. Next Generation Sequencing (NGS) promises to accelerate this process and revolutionize medicine. Using advanced technologies to identify variants in whole genomes, NGS allows prediction and diagnosis of disease and personalized treatments to the individual. Unfortunately, NGS identifies tens of thousands of variants: some real, some false positive and some false negative. Many variants can be excluded by comparing across genomes and rationalizing using different filter criteria. However, the list of potential variants involved in a given genetic disease remains several thousand long and we are back to looking for a needle in a haystack. Overcoming this barrier would be a monumental advance in personalized medicine. We propose that the clues to finding the affective gene are hidden in massive mines of biomedical data online. Using this and the genetic information unique to each patient, we created an online platform that creates ‘biograms’ – personalized reports that identify disease causing genes based on the symptoms of a genetic disease.
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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 | 0.005 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.022 | 0.012 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".