Extracting Knowledge from Genomic Experiments by Incorporating the Biomedical Literature
Bibliographic record
Abstract
We present a technique to extract relevant information from the literature to aid in the analysis of a typical genomics data set. Analysis was conducted using PDQ_MED, a program based on the assumption that if two genes are found to be related under an experimental paradigm, such as a gene chip experiment, then any literature which relates the two genes is of interest. PDQ_MED searches MEDLINE for abstracts that contain two or more of the terms in the user’s query set. For this paper, we have used PDQ_MED to analyze 160 genes up-regulated in acute myeloid leukemia (AML) from the NCI-60 dataset. PDQ_MED executed 12,880 queries to MEDLINE and identified nearly 300,000 abstracts that refer to at least one of the 160 terms. PDQ_MED identified and analyzed a set of 81 terms that can be grouped together via the literature. In addition, there is literature directly linking 52 of the terms with AML. Overall, the literature analysis identified 1028 sentences that directly relate two or more of the query genes.
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 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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.030 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".