Pharmacodb-1.0.0
Bibliographic record
Abstract
PharmacoDB allows scientists to search across publicly available datasets to find instances where a drug or cell line of interest has been profiled, and to view and compare the dose-response data for a specific cell line - drug pair from any of the studies included in the database. We are providing full access to the contents of the first stable release of PharmacoDB and are distributing the data as a MySQL dump. Making our research fully reproducible is part of the mission of the Haibe-Kains laboratory. The existing dump can be made <em>PostgreSQL</em> compatible by following the instructions below: Step 1: Install Docker (https://www.docker.com/) Step 2: Run the commands below sudo docker pull bhklab/pharmacodb:1.0.0 sudo docker run -d -p 3000:3000 -v ~:/home -it --name pharmacodb_v1.0.0 --entrypoint /bin/bash bhklab/pharmacodb:1.0.0 sudo docker exec -it --user root pharmacodb_v1.0.0 /usr/src/app/data/mysql_to_postgres The new PostgreSQL compatible dump will be available in your home directory as 'pharmacodb_development.psql'. <em>Update</em> <em>Version 1 of this dataset did not contain the HARA cell-line annotation correction (tissue type is lung and not breast). Our apologies for any inconvenience. Please use and cite Version 2 instead.</em> For more information, please visit https://pharmacodb.pmgenomics.ca
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 | 0.000 | 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 | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.047 | 0.033 |
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; both teacher heads agree on what is shown here.
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".