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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".