Cell Maps for Artificial Intelligence - Data Release
Bibliographic record
Abstract
This collection is the 0.5 alpha data release of the the Cell Maps for Artificial Intelligence (CM4AI) Functional Genomics Data Generation Project, a component of the U.S. National Institute of Health’s (NIH) Bridge2AI program. CM4AI’s objective is to deliver machine-readable hierarchical maps of cell architecture as AI-Ready data produced from multimodal interrogation of 100 chromatin modifiers and 100 metabolic enzymes involved in cancer, neuropsychiatric, and cardiac disorders in disease-relevant cell lines under perturbed and unperturbed conditions, utilizing state-of-the-art mass spectrometry based proteomics, spatial proteomics / cell imaging, and genetic perturbations using CRISPR. CM4AI input data streams are generated using immunofluorescence (IF) subcellular microscopy for spatial proteomics data; affinity purification mass spectroscopy (AP-MS) and size exclusion mass spectroscopy (SEC-MS) methods for protein-protein interaction (PPI) data; and single-cell CRISPR-Cas perturbation screens by cell type. Input data streams are integrated via the multi-scale integrated cell (MuSIC) software pipeline employing deep learning models and community detection algorithms2, and output cell maps are packaged with provenance graphs and rich metadata as AI-Ready datasets in RO-Crate format using an extended, client-server version of the FAIRSCAPE framework. This data is Copyright (c) 2024 The Regents of the University of California except where otherwise noted. Spatial proteomics raw image data is copyright (c) 2024 The Board of Trustees of the Leland Stanford Junior University. It is licensed for reuse under Creative Commons Attribution ShareAlike NonCommercial 4.0 International License (https://creativecommons.org/licenses/by-nc-sa/4.0/). Attribution is required to the copyright holders and the authors. Any publications referencing this data or derived products. should cite the related article as well as directly citing this data collection.
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 | 0.014 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.228 | 0.249 |
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".