AI-predicted spatial transcriptomics unlocks breast cancer biomarkers from pathology.
- DOI
- 10.1016/j.cell.2026.04.023
- Published
- 2026 Jul 9
- Container
- Cell
- Publisher
- Not recorded
- Open access
- yes
Credibility signals
limited evidence Score 45/100 under policy 1.0.0. This is a metadata assessment, not a judgment of the paper's conclusions.
Show all credibility signals
- cautionDOI registered: No matching Crossref record was present in this response.
- cautionDOI resolves: No matching Crossref record was present in this response.
- not scoredDirectory of Open Access Journals: No matching DOAJ record was present in this response. No allow-list match; this is not evidence of low credibility.
- not scoredMEDLINE indexed: Not checked or no result supplied; no credibility inference made.
- not scoredOpenAlex core source: Not checked or no result supplied; no credibility inference made.
- not scoredKnown publisher allow-list: Not checked or no result supplied; no credibility inference made.
- not scoredROR affiliation: Not checked or no result supplied; no credibility inference made.
- not scoredRetraction Watch retraction: No retraction notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredRetraction Watch expression of concern: No expression of concern notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredRetraction Watch correction: No correction notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredRetraction Watch reinstatement: No reinstatement notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- supportingOpen access status: Normalized open-access status: open.
- not scoredPublication license: Not checked or no result supplied; no credibility inference made.
- not scoredPublication version: A publication version was supplied but is not scored.
- cautionMetadata completeness: 5 of 6 scored descriptive metadata groups are present; missing fields increase uncertainty.
Cite this work
BibTeX
@article{allodium:10.1016/j.cell.2026.04.023,
title = {AI-predicted spatial transcriptomics unlocks breast cancer biomarkers from pathology.},
author = {Shulman ED and Campagnolo EM and Lodha R and Chung Y and Stemmer A and Cantore T and Ru B and Chang TG and Biswas S and Dhruba SR and Patiyal S and Patkar S and Wang A and Barman RK and Wang C and Paul R and Kalisetty SC and Hu T and Nasrallah MP and Patrick E and Yang J and Yuan Y and Sargsyan K and Plotkin A and Rajagopal PS and Sammut SJ and Lipkowitz S and Jiang P and Caldas C and Knott SRV and Aldape K and Lee JS and Hoang DT and Ruppin E},
year = {2026},
journal = {Cell},
doi = {10.1016/j.cell.2026.04.023},
url = {https://doi.org/10.1016/j.cell.2026.04.023}
}RIS
TY - JOUR TI - AI-predicted spatial transcriptomics unlocks breast cancer biomarkers from pathology. AU - Shulman ED AU - Campagnolo EM AU - Lodha R AU - Chung Y AU - Stemmer A AU - Cantore T AU - Ru B AU - Chang TG AU - Biswas S AU - Dhruba SR AU - Patiyal S AU - Patkar S AU - Wang A AU - Barman RK AU - Wang C AU - Paul R AU - Kalisetty SC AU - Hu T AU - Nasrallah MP AU - Patrick E AU - Yang J AU - Yuan Y AU - Sargsyan K AU - Plotkin A AU - Rajagopal PS AU - Sammut SJ AU - Lipkowitz S AU - Jiang P AU - Caldas C AU - Knott SRV AU - Aldape K AU - Lee JS AU - Hoang DT AU - Ruppin E PY - 2026 JO - Cell DO - 10.1016/j.cell.2026.04.023 UR - https://doi.org/10.1016/j.cell.2026.04.023 ER -
APA
ED, S., EM, C., R, L., Y, C., A, S., T, C., B, R., TG, C., S, B., SR, D., S, P., S, P., A, W., RK, B., C, W., R, P., SC, K., T, H., MP, N., E, P., J, Y., Y, Y., K, S., A, P., PS, R., SJ, S., S, L., P, J., C, C., SRV, K., K, A., JS, L., DT, H., & E, R. (2026). AI-predicted spatial transcriptomics unlocks breast cancer biomarkers from pathology.. Cell. https://doi.org/10.1016/j.cell.2026.04.023
Source records
- pubmed · retrieved 2026-09-25T22:32:00.592Z