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Deep Learning Enabled 3D Multi-Omic Analysis Reveals Molecular Signatures of Heterogeneous Response to Chemotherapy in Pancreatic Cancer.

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Title: Deep Learning Enabled 3D Multi-Omic Analysis Reveals Molecular Signatures of Heterogeneous Response to Chemotherapy in Pancreatic Cancer.
Authors: Forjaz A; Department of Chemical & Biomolecular Engineering, Johns Hopkins University, Baltimore, MD.; The Johns Hopkins Institute for NanoBioTechnology, Johns Hopkins University, Baltimore, MD., Mojdeganlou H; Department of Pathology, The Sol Goldman Pancreatic Cancer Research Center, Johns Hopkins University, Baltimore, MD., Valentin A; Department of Oncology, Johns Hopkins University, Baltimore, MD., Wetzel M; Department of Oncology, Johns Hopkins University, Baltimore, MD., Lvovs D; Institute for Genome Sciences, University of Maryland, Baltimore, MD., Deshpande A; Department of Oncology, Johns Hopkins University, Baltimore, MD., Shin SM; Department of Oncology, Johns Hopkins University, Baltimore, MD., Piya S; Department of Gastrointestinal Radiation Oncology, University of Texas MD Anderson Cancer Center, Houston, TX., Rajapakshe KI; Department of Gastrointestinal Radiation Oncology, University of Texas MD Anderson Cancer Center, Houston, TX., Guerrero PA; Department of Gastrointestinal Radiation Oncology, University of Texas MD Anderson Cancer Center, Houston, TX., Pedro BA; Department of Pathology, The Sol Goldman Pancreatic Cancer Research Center, Johns Hopkins University, Baltimore, MD., Sidiropoulos DN; Department of Oncology, Johns Hopkins University, Baltimore, MD., Wu PH; The Johns Hopkins Institute for NanoBioTechnology, Johns Hopkins University, Baltimore, MD., Pagan VB; Department of Gastrointestinal Radiation Oncology, University of Texas MD Anderson Cancer Center, Houston, TX., Wirtz D; Department of Chemical & Biomolecular Engineering, Johns Hopkins University, Baltimore, MD.; The Johns Hopkins Institute for NanoBioTechnology, Johns Hopkins University, Baltimore, MD.; Department of Pathology, The Sol Goldman Pancreatic Cancer Research Center, Johns Hopkins University, Baltimore, MD.; Department of Oncology, Johns Hopkins University, Baltimore, MD., Fertig EJ; Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, MD.; Division of Hematology / Oncology, Department of Medicine, University of Maryland School of Medicine, Baltimore, MD.; Greenbaum Comprehensive Cancer Center, University of Maryland School of Medicine, Baltimore, MD.; UM-Institute for Health Computing, University of Maryland School of Medicine, North Bethesda, MD., Kagohara LT; Department of Oncology, Johns Hopkins University, Baltimore, MD., Ho WJ; Department of Oncology, Johns Hopkins University, Baltimore, MD., Kiemen AL; Department of Chemical & Biomolecular Engineering, Johns Hopkins University, Baltimore, MD.; The Johns Hopkins Institute for NanoBioTechnology, Johns Hopkins University, Baltimore, MD.; Department of Pathology, The Sol Goldman Pancreatic Cancer Research Center, Johns Hopkins University, Baltimore, MD.; Department of Oncology, Johns Hopkins University, Baltimore, MD.; Department of Functional Anatomy & Evolution, Johns Hopkins University, Baltimore, MD, USA., Wood LD; Department of Pathology, The Sol Goldman Pancreatic Cancer Research Center, Johns Hopkins University, Baltimore, MD.; Department of Oncology, Johns Hopkins University, Baltimore, MD., Christenson ES; Department of Oncology, Johns Hopkins University, Baltimore, MD., Freed-Pastor WA; Department of Medical Oncology, Dana-Farber Cancer Institute and Harvard Medical School, Boston, Massachusetts., Iacobuzio-Donahue CA; Human Oncology and Pathogenesis Program, Memorial Sloan Kettering Cancer Center, New York City, NY, USA.; David M. Rubenstein Center for Pancreatic Cancer Research, Memorial Sloan Kettering Cancer Center, New York City, NY, USA.; Department of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, 417 Est 68th Street New York, NY 10065, USA., Karchin R; The Institute for Computational Medicine, The Johns Hopkins University, Baltimore, MD 21218, USA.; Departments of Biomedical Engineering, and Computer Science, The Johns Hopkins University, Baltimore, MD 21218, USA., Karnoub ER; Human Oncology and Pathogenesis Program, Memorial Sloan Kettering Cancer Center, New York City, NY, USA.; David M. Rubenstein Center for Pancreatic Cancer Research, Memorial Sloan Kettering Cancer Center, New York City, NY, USA.; Department of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, 417 Est 68th Street New York, NY 10065, USA., Maitra A; Departments of Pathology and Medicine, New York University Grossman School of Medicine and Perlmutter Cancer Center, New York, NY, USA., Melchor J; Human Oncology and Pathogenesis Program, Memorial Sloan Kettering Cancer Center, New York City, NY, USA.; David M. Rubenstein Center for Pancreatic Cancer Research, Memorial Sloan Kettering Cancer Center, New York City, NY, USA., Park W; David M. Rubenstein Center for Pancreatic Cancer Research, Memorial Sloan Kettering Cancer Center, New York City, NY, USA.; Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA., Matos-Romero V; Department of Chemical & Biomolecular Engineering, Johns Hopkins University, Baltimore, MD.; The Johns Hopkins Institute for NanoBioTechnology, Johns Hopkins University, Baltimore, MD., O'Reilly E; David M. Rubenstein Center for Pancreatic Cancer Research, Memorial Sloan Kettering Cancer Center, New York City, NY, USA.; Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA., Soares KC; David M. Rubenstein Center for Pancreatic Cancer Research, Memorial Sloan Kettering Cancer Center, New York City, NY, USA.; Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
Corporate Authors: Demystifying Pancreatic Cancer Therapies TeamLab
Source: BioRxiv : the preprint server for biology [bioRxiv] 2026 Mar 05. Date of Electronic Publication: 2026 Mar 05.
Publication Type: Journal Article; Preprint
Journal Info: Country of Publication: United States NLM ID: 101680187 Publication Model: Electronic Cited Medium: Internet ISSN: 2692-8205 (Electronic) Linking ISSN: 26928205 NLM ISO Abbreviation: bioRxiv Subsets: PubMed not MEDLINE
Database: MEDLINE Ultimate
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  Data: Deep Learning Enabled 3D Multi-Omic Analysis Reveals Molecular Signatures of Heterogeneous Response to Chemotherapy in Pancreatic Cancer.
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Department of Pathology, The Sol Goldman Pancreatic Cancer Research Center, Johns Hopkins University, Baltimore, MD.; Department of Oncology, Johns Hopkins University, Baltimore, MD.<br /><searchLink fieldCode="AU" term="%22Christenson+ES%22">Christenson ES</searchLink>; Department of Oncology, Johns Hopkins University, Baltimore, MD.<br /><searchLink fieldCode="AU" term="%22Freed-Pastor+WA%22">Freed-Pastor WA</searchLink>; Department of Medical Oncology, Dana-Farber Cancer Institute and Harvard Medical School, Boston, Massachusetts.<br /><searchLink fieldCode="AU" term="%22Iacobuzio-Donahue+CA%22">Iacobuzio-Donahue CA</searchLink>; Human Oncology and Pathogenesis Program, Memorial Sloan Kettering Cancer Center, New York City, NY, USA.; David M. 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Rubenstein Center for Pancreatic Cancer Research, Memorial Sloan Kettering Cancer Center, New York City, NY, USA.; Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
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