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 PostgreSQL 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'. Update 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. 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 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 | 0.012 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.008 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.219 | 0.301 |
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".