Paper · 41867770
Deep Learning Enabled 3D Multi-Omic Analysis Reveals Molecular Signatures of Heterogeneous Response to Chemotherapy in Pancreatic Cancer.
Forjaz A and 31 others. bioRxiv, 2026. Cited by 1 paper, by iCite.
11 sourcespapers-2026-11-12-0.1.0
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ConductScience Library. Deep Learning Enabled 3D Multi-Omic Analysis Reveals Molecular Signatures of Heterogeneous Response to Chemotherapy in Pancreatic Cancer. Papers, record 41867770. Release papers-2026-11-12-0.1.0, 12 Nov 2026. https://conductscience.org/library/papers/41867770
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Paper
PubMed's record of the paper, with iCite's citation figures. No abstract is shown.
- Title
- Deep Learning Enabled 3D Multi-Omic Analysis Reveals Molecular Signatures of Heterogeneous Response to Chemotherapy in Pancreatic Cancer.
- PubMed
Topics0
NLM's MeSH indexing of the paper, its major topics first.
None in this release.
Grants5
Each grant PubMed's record writes, matched to the NIH project NIH's grant and paper links name for this paper, and each of those links the record does not write.
| Grant, as the record writes it | Agency | Country | NIH project | Stated by | Source |
|---|---|---|---|---|---|
| K08 CA259621 | NCI NIH HHS | United States | K08CA259621 | PubMed | NIH RePORTER |
| T32 CA193145 | NCI NIH HHS | United States | T32CA193145 | PubMed | NIH RePORTER |
| U54 CA268083 | NCI NIH HHS | United States | U54CA268083 | PubMed | NIH RePORTER |
| U24 CA284156 | NCI NIH HHS | United States | U24CA284156 | PubMed | NIH RePORTER |
| S10 OD034407 | NIH HHS | United States | S10OD034407 | PubMed | NIH RePORTER |
Trials0
The trials whose ClinicalTrials.gov record lists the paper, and the trial numbers the paper's PubMed record names.
None in this release.
Comments and corrections0
PubMed's comments and corrections, NLM's own indexing: retractions first, then expressions of concern, errata and updates, and comments last.
None in this release.
References45
The papers the reference list in PubMed's record names, those in our copy of PubMed, in the list's order.
| Order | PMID | Title | Journal | Year | In Papers | Source |
|---|---|---|---|---|---|---|
| 1 | 41,592,755 | Computational Modeling of Cellular Influence Delineates Functionally Relevant and Distinct Cellular Neighborhoods in Primary and Metastatic Pancreatic Ductal Adenocarcinoma. | Cancer Immunol Res | 2026 | https://conductscience.org/library/papers/41592755 | PubMed |
| 2 | 34,889,852 | Pathologic Examination of Pancreatic Specimens Resected for Treated Pancreatic Ductal Adenocarcinoma: Recommendations From the Pancreatobiliary Pathology Society. | Am J Surg Pathol | 2022 | https://conductscience.org/library/papers/34889852 | PubMed |
| 3 | 38,424,144 | Semi-automated approaches for interrogating spatial heterogeneity of tissue samples. | Sci Rep | 2024 | https://conductscience.org/library/papers/38424144 | PubMed |
| 4 | 40,713,971 | Therapeutic targeting of syndecan-1 axis overcomes acquired resistance to KRAS-targeted therapy in gastrointestinal cancers. | Cell Rep Med | 2025 | https://conductscience.org/library/papers/40713971 | PubMed |
| 5 | 39,278,279 | Dual Targeting of Syndecan-1 and Glucose Transporter-1 With a Novel Lipid-Based Delivery System Enhances Therapeutic Efficacy and Overcomes Chemoresistance in Pancreatic Ductal Adenocarcinoma. | Gastroenterology | 2025 | https://conductscience.org/library/papers/39278279 | PubMed |
| 6 | 19,460,966 | Inhibition of Hedgehog signaling enhances delivery of chemotherapy in a mouse model of pancreatic cancer. | Science | 2009 | https://conductscience.org/library/papers/19460966 | PubMed |
| 7 | 39,636,224 | Human Pancreatic Cancer Single-Cell Atlas Reveals Association of CXCL10+ Fibroblasts and Basal Subtype Tumor Cells. | Clin Cancer Res | 2025 | https://conductscience.org/library/papers/39636224 | PubMed |
| 8 | 29,409,532 | SCANPY: large-scale single-cell gene expression data analysis. | Genome Biol | 2018 | https://conductscience.org/library/papers/29409532 | PubMed |
| 9 | 31,688,603 | Expression of Epithelial-Mesenchymal Transition Markers in Treated Pancreatic Ductal Adenocarcinoma. | Pancreas | 2019 | https://conductscience.org/library/papers/31688603 | PubMed |
| 10 | 39,116,880 | PanIN and CAF transitions in pancreatic carcinogenesis revealed with spatial data integration. | Cell Syst | 2024 | https://conductscience.org/library/papers/39116880 | PubMed |
1 to 10 of 45
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The papers in our copy of PubMed whose reference list names this one, newest first. iCite, which reads more sources, counts 1 citation.
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Genes0
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None in this release.
Drugs0
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Data0
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Data dates
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- Trials
- 2026-11-12
- Gene search
- 2026-11-11
- Pipelines
- 2026-09-18
Sources 11
- ChEMBL, through Open TargetsCC BY-SA 3.0
- ClinicalTrials.govNo license stated
- HGNCCC0
- iCiteCC BY 4.0
- NCBI GenePublic domain (US government)
- NIH RePORTER's grant and paper linksPublic domain
- Open TargetsCC0 1.0
- PMC's ID filePublic domain (US government)
- PubMedNLM's terms
- PubTator 3Public domain
- UniProtCC BY 4.0
Each source's terms
- ChEMBL, through Open TargetsCC BY-SA 3.0, for the parts from ChEMBL. Read on 8 Nov 2026 at https://platform-docs.opentargets.org/licence
- ClinicalTrials.govnone stated
- HGNCCC0: https://www.genenames.org/about/license/
- iCiteCC BY 4.0
- NCBI GeneNone named for the file. NCBI says information created by or for the US government on its site is in the public domain, and asks that NLM be acknowledged: https://www.ncbi.nlm.nih.gov/home/about/policies/
- NIH RePORTER's grant and paper linksPublic domain (NIH RePORTER)
- Open TargetsCC0 1.0. Open Targets asks that users cite its latest paper: Buniello et al. 2025, Open Targets Platform: facilitating therapeutic hypotheses building in drug discovery, Nucleic Acids Research 53(D1):D1467, PMID 39657122. Read on 8 Nov 2026 at https://platform-docs.opentargets.org/licence
- PMC's ID fileNone named for the file. NCBI says information created by or for the US government on its site is in the public domain, and asks that NLM be acknowledged: https://www.ncbi.nlm.nih.gov/home/about/policies/
- PubMedNLM's terms
- PubTator 3Public domain
- UniProtCC BY 4.0. UniProt's licence file: "All databases and documents in the UniProt FTP directory may be copied and redistributed freely, without advance permission, provided that this copyright statement is reproduced with each copy." UniProt asks that users cite its latest paper: The UniProt Consortium, UniProt: the Universal Protein Knowledgebase in 2025, Nucleic Acids Research 53:D609-D617, https://doi.org/10.1093/nar/gkae1010. Read on 9 Nov 2026 at https://www.uniprot.org/help/license
ClinicalTrials.gov's data carry an international copyright outside the United States: https://clinicaltrials.gov/about-site/terms-conditions.
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