{"entity": "researcher", "timestamp": "2026-03-09T08:59:49.294Z", "family": "Meimetis", "given": "Nikolaos", "initials": "N", "orcid": "0000-0003-2333-0187", "affiliations": ["Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA."], "links": {"self": {"href": "https://publications-affiliated.scilifelab.se/researcher/e0e968cb5cc44b52818647cea77762d9.json"}, "display": {"href": "https://publications-affiliated.scilifelab.se/researcher/e0e968cb5cc44b52818647cea77762d9"}}, "publications": [{"entity": "publication", "iuid": "86edd1bfedc24087afc1752b92c4dbab", "links": {"self": {"href": "https://publications-affiliated.scilifelab.se/publication/86edd1bfedc24087afc1752b92c4dbab.json"}, "display": {"href": "https://publications-affiliated.scilifelab.se/publication/86edd1bfedc24087afc1752b92c4dbab"}}, "title": "Towards an interpretable deep learning model of cancer.", "authors": [{"family": "Nilsson", "given": "Avlant", "initials": "A", "orcid": "0000-0002-9476-4516", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/f2e21dbc1c624f6a841c59e959e948e4.json"}}, {"family": "Meimetis", "given": "Nikolaos", "initials": "N", "orcid": "0000-0003-2333-0187", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/e0e968cb5cc44b52818647cea77762d9.json"}}, {"family": "Lauffenburger", "given": "Douglas A", "initials": "DA", "orcid": "0000-0002-0050-989X", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/ac0a09cb7c9c49de95b660756a4e5464.json"}}], "type": "journal article", "published": "2025-02-14", "journal": {"title": "NPJ Precis Oncol", "issn": "2397-768X", "issn-l": null, "volume": "9", "issue": "1", "pages": "46"}, "abstract": "Cancer is a manifestation of dysfunctional cell states. It emerges from an interplay of intrinsic and extrinsic factors that disrupt cellular dynamics, including genetic and epigenetic alterations, as well as the tumor microenvironment. This complexity can make it challenging to infer molecular causes for treating the disease. This may be addressed by system-wide computer models of cells, as they allow rapid generation and testing of hypotheses that would be too slow or impossible to perform in the laboratory and clinic. However, so far, such models have been impeded by both experimental and computational limitations. In this perspective, we argue that they can now be achieved using deep learning algorithms to integrate omics data and prior knowledge of molecular networks. Such models would have many applications in precision oncology, e.g., for identifying drug targets and biomarkers, predicting resistance mechanisms and toxicity effects of drugs, or simulating cell-cell interactions in the microenvironment.", "doi": "10.1038/s41698-025-00822-y", "pmid": "39948231", "labels": {"Avlant Nilsson": null, "DDLS Fellow": null}, "xrefs": [{"db": "pmc", "key": "PMC11825879"}, {"db": "pii", "key": "10.1038/s41698-025-00822-y"}], "notes": [], "created": "2025-03-20T11:42:04.334Z", "modified": "2025-03-21T10:37:58.957Z"}, {"entity": "publication", "iuid": "fbb534254dff438b83a95bb8dbbe741d", "links": {"self": {"href": "https://publications-affiliated.scilifelab.se/publication/fbb534254dff438b83a95bb8dbbe741d.json"}, "display": {"href": "https://publications-affiliated.scilifelab.se/publication/fbb534254dff438b83a95bb8dbbe741d"}}, "title": "Protocol to infer off-target effects of drugs on cellular signaling using interactome-based deep learning.", "authors": [{"family": "Meimetis", "given": "Nikolaos", "initials": "N", "orcid": "0000-0003-2333-0187", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/e0e968cb5cc44b52818647cea77762d9.json"}}, {"family": "Lauffenburger", "given": "Douglas A", "initials": "DA", "orcid": "0000-0002-0050-989X", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/ac0a09cb7c9c49de95b660756a4e5464.json"}}, {"family": "Nilsson", "given": "Avlant", "initials": "A", "orcid": "0000-0002-9476-4516", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/f2e21dbc1c624f6a841c59e959e948e4.json"}}], "type": "journal article", "published": "2025-01-16", "journal": {"title": "STAR Protoc", "issn": "2666-1667", "issn-l": null, "volume": "6", "issue": "1", "pages": "103573"}, "abstract": "Drugs that target specific proteins often have off-target effects. We present a protocol using artificial neural networks to model cellular transcriptional responses to drugs, aiming to understand their mechanisms of action. We detail steps for predicting transcriptional activities, inferring drug-target interactions, and explaining the off-target mechanism of action. As a case study, we analyze the off-target effects of lestaurtinib on FOXM1 in the A375 cell line. For complete details on the use and execution of this protocol, please refer to Meimetis et al.1.", "doi": "10.1016/j.xpro.2024.103573", "pmid": "39823233", "labels": {"Avlant Nilsson": null, "DDLS Fellow": null}, "xrefs": [{"db": "pmc", "key": "PMC11786766"}, {"db": "pii", "key": "S2666-1667(24)00738-X"}], "notes": [], "created": "2025-03-20T10:57:36.917Z", "modified": "2025-03-21T13:16:38.586Z"}, {"entity": "publication", "iuid": "cee11f4ab587455b89473bdc8546bab5", "links": {"self": {"href": "https://publications-affiliated.scilifelab.se/publication/cee11f4ab587455b89473bdc8546bab5.json"}, "display": {"href": "https://publications-affiliated.scilifelab.se/publication/cee11f4ab587455b89473bdc8546bab5"}}, "title": "Author Correction: AutoTransOP: translating omics signatures without orthologue requirements using deep learning.", "authors": [{"family": "Meimetis", "given": "Nikolaos", "initials": "N", "orcid": "0000-0003-2333-0187", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/e0e968cb5cc44b52818647cea77762d9.json"}}, {"family": "Pullen", "given": "Krista M", "initials": "KM", "orcid": "0000-0002-4857-8907", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/6930c9935ef740479a3052b4796d7d6d.json"}}, {"family": "Zhu", "given": "Daniel Y", "initials": "DY"}, {"family": "Nilsson", "given": "Avlant", "initials": "A", "orcid": "0000-0002-9476-4516", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/f2e21dbc1c624f6a841c59e959e948e4.json"}}, {"family": "Hoang", "given": "Trong Nghia", "initials": "TN"}, {"family": "Magliacane", "given": "Sara", "initials": "S"}, {"family": "Lauffenburger", "given": "Douglas A", "initials": "DA", "orcid": "0000-0002-0050-989X", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/ac0a09cb7c9c49de95b660756a4e5464.json"}}], "type": "published erratum", "published": "2024-12-13", "journal": {"title": "NPJ Syst Biol Appl", "issn": "2056-7189", "issn-l": "2056-7189", "volume": "10", "issue": "1", "pages": "148"}, "abstract": null, "doi": "10.1038/s41540-024-00456-z", "pmid": "39672816", "labels": {"Avlant Nilsson": null, "DDLS Fellow": null}, "xrefs": [{"db": "pmc", "key": "PMC11645403"}, {"db": "pii", "key": "10.1038/s41540-024-00456-z"}], "notes": [], "created": "2025-03-20T11:12:01.570Z", "modified": "2025-03-21T10:38:14.626Z"}, {"entity": "publication", "iuid": "e8139e5e7ef04acd839fd3906a2e385e", "links": {"self": {"href": "https://publications-affiliated.scilifelab.se/publication/e8139e5e7ef04acd839fd3906a2e385e.json"}, "display": {"href": "https://publications-affiliated.scilifelab.se/publication/e8139e5e7ef04acd839fd3906a2e385e"}}, "title": "Inference of drug off-target effects on cellular signaling using interactome-based deep learning.", "authors": [{"family": "Meimetis", "given": "Nikolaos", "initials": "N", "orcid": "0000-0003-2333-0187", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/e0e968cb5cc44b52818647cea77762d9.json"}}, {"family": "Lauffenburger", "given": "Douglas A", "initials": "DA", "orcid": "0000-0002-0050-989X", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/ac0a09cb7c9c49de95b660756a4e5464.json"}}, {"family": "Nilsson", "given": "Avlant", "initials": "A", "orcid": "0000-0002-9476-4516", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/f2e21dbc1c624f6a841c59e959e948e4.json"}}], "type": "journal article", "published": "2024-04-19", "journal": {"title": "iScience", "issn": "2589-0042", "issn-l": null, "volume": "27", "issue": "4", "pages": "109509"}, "abstract": "Many diseases emerge from dysregulated cellular signaling, and drugs are often designed to target specific signaling proteins. Off-target effects are, however, common and may ultimately result in failed clinical trials. Here we develop a computer model of the cell's transcriptional response to drugs for improved understanding of their mechanisms of action. The model is based on ensembles of artificial neural networks and simultaneously infers drug-target interactions and their downstream effects on intracellular signaling. With this, it predicts transcription factors' activities, while recovering known drug-target interactions and inferring many new ones, which we validate with an independent dataset. As a case study, we analyze the effects of the drug Lestaurtinib on downstream signaling. Alongside its intended target, FLT3, the model predicts an inhibition of CDK2 that enhances the downregulation of the cell cycle-critical transcription factor FOXM1. Our approach can therefore enhance our understanding of drug signaling for therapeutic design.", "doi": "10.1016/j.isci.2024.109509", "pmid": "38591003", "labels": {"Avlant Nilsson": null, "DDLS Fellow": null}, "xrefs": [{"db": "pmc", "key": "PMC11000001"}, {"db": "pii", "key": "S2589-0042(24)00730-2"}], "notes": [], "created": "2025-03-20T10:57:42.060Z", "modified": "2025-03-21T13:15:57.841Z"}, {"entity": "publication", "iuid": "b430290ce94247c285d5680d2ac53006", "links": {"self": {"href": "https://publications-affiliated.scilifelab.se/publication/b430290ce94247c285d5680d2ac53006.json"}, "display": {"href": "https://publications-affiliated.scilifelab.se/publication/b430290ce94247c285d5680d2ac53006"}}, "title": "AutoTransOP: translating omics signatures without orthologue requirements using deep learning.", "authors": [{"family": "Meimetis", "given": "Nikolaos", "initials": "N", "orcid": "0000-0003-2333-0187", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/e0e968cb5cc44b52818647cea77762d9.json"}}, {"family": "Pullen", "given": "Krista M", "initials": "KM", "orcid": "0000-0002-4857-8907", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/6930c9935ef740479a3052b4796d7d6d.json"}}, {"family": "Zhu", "given": "Daniel Y", "initials": "DY"}, {"family": "Nilsson", "given": "Avlant", "initials": "A", "orcid": "0000-0002-9476-4516", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/f2e21dbc1c624f6a841c59e959e948e4.json"}}, {"family": "Hoang", "given": "Trong Nghia", "initials": "TN"}, {"family": "Magliacane", "given": "Sara", "initials": "S"}, {"family": "Lauffenburger", "given": "Douglas A", "initials": "DA", "orcid": "0000-0002-0050-989X", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/ac0a09cb7c9c49de95b660756a4e5464.json"}}], "type": "journal article", "published": "2024-01-29", "journal": {"title": "NPJ Syst Biol Appl", "issn": "2056-7189", "issn-l": "2056-7189", "volume": "10", "issue": "1", "pages": "13"}, "abstract": "The development of therapeutics and vaccines for human diseases requires a systematic understanding of human biology. Although animal and in vitro culture models can elucidate some disease mechanisms, they typically fail to adequately recapitulate human biology as evidenced by the predominant likelihood of clinical trial failure. To address this problem, we developed AutoTransOP, a neural network autoencoder framework, to map omics profiles from designated species or cellular contexts into a global latent space, from which germane information for different contexts can be identified without the typically imposed requirement of matched orthologues. This approach was found in general to perform at least as well as current alternative methods in identifying animal/culture-specific molecular features predictive of other contexts-most importantly without requiring homology matching. For an especially challenging test case, we successfully applied our framework to a set of inter-species vaccine serology studies, where 1-to-1 mapping between human and non-human primate features does not exist.", "doi": "10.1038/s41540-024-00341-9", "pmid": "38287079", "labels": {"Avlant Nilsson": null, "DDLS Fellow": null}, "xrefs": [{"db": "pmc", "key": "PMC10825146"}, {"db": "pii", "key": "10.1038/s41540-024-00341-9"}], "notes": [], "created": "2025-03-20T10:57:39.228Z", "modified": "2025-03-21T13:16:11.249Z"}, {"entity": "publication", "iuid": "19e635d203f04a55a0f80bdc96d6d64f", "links": {"self": {"href": "https://publications-affiliated.scilifelab.se/publication/19e635d203f04a55a0f80bdc96d6d64f.json"}, "display": {"href": "https://publications-affiliated.scilifelab.se/publication/19e635d203f04a55a0f80bdc96d6d64f"}}, "title": "Autoencoder Model for Translating Omics Signatures", "authors": [{"family": "Meimetis", "given": "Nikolaos", "initials": "N", "orcid": "0000-0003-2333-0187", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/e0e968cb5cc44b52818647cea77762d9.json"}}, {"family": "Pullen", "given": "Krista M", "initials": "KM", "orcid": "0000-0002-4857-8907", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/6930c9935ef740479a3052b4796d7d6d.json"}}, {"family": "Zhu", "given": "Daniel Y", "initials": "DY"}, {"family": "Nilsson", "given": "Avlant", "initials": "A", "orcid": "0000-0002-9476-4516", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/f2e21dbc1c624f6a841c59e959e948e4.json"}}, {"family": "Hoang", "given": "Trong Nghia", "initials": "TN"}, {"family": "Magliacane", "given": "Sara", "initials": "S"}, {"family": "Lauffenburger", "given": "Douglas A", "initials": "DA", "orcid": "0000-0002-0050-989X", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/ac0a09cb7c9c49de95b660756a4e5464.json"}}], "type": "posted-content", "published": "2023-06-11", "journal": {"title": null, "issn": null, "issn-l": null, "volume": null, "issue": null, "pages": null}, "abstract": null, "doi": "10.1101/2023.06.08.544243", "pmid": null, "labels": {"Avlant Nilsson": null, "DDLS Fellow": null}, "xrefs": [], "notes": [], "created": "2025-03-20T11:10:27.535Z", "modified": "2025-03-21T10:37:20.777Z"}, {"entity": "publication", "iuid": "d963d1f6aaad43dd91df7a8045deb096", "links": {"self": {"href": "https://publications-affiliated.scilifelab.se/publication/d963d1f6aaad43dd91df7a8045deb096.json"}, "display": {"href": "https://publications-affiliated.scilifelab.se/publication/d963d1f6aaad43dd91df7a8045deb096"}}, "title": "Artificial neural networks enable genome-scale simulations of intracellular signaling.", "authors": [{"family": "Nilsson", "given": "Avlant", "initials": "A", "orcid": "0000-0002-9476-4516", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/f2e21dbc1c624f6a841c59e959e948e4.json"}}, {"family": "Peters", "given": "Joshua M", "initials": "JM", "orcid": "0000-0001-9163-6706", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/7cc62a8588fb4ccc80d4cb52fd584a0f.json"}}, {"family": "Meimetis", "given": "Nikolaos", "initials": "N", "orcid": "0000-0003-2333-0187", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/e0e968cb5cc44b52818647cea77762d9.json"}}, {"family": "Bryson", "given": "Bryan", "initials": "B", "orcid": "0000-0003-1716-6712", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/53a00ca82242485d8d0c88ed953bd537.json"}}, {"family": "Lauffenburger", "given": "Douglas A", "initials": "DA", "orcid": "0000-0002-0050-989X", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/ac0a09cb7c9c49de95b660756a4e5464.json"}}], "type": "journal article", "published": "2022-06-02", "journal": {"title": "Nat Commun", "issn": "2041-1723", "issn-l": "2041-1723", "volume": "13", "issue": "1", "pages": "3069"}, "abstract": "Mammalian cells adapt their functional state in response to external signals in form of ligands that bind receptors on the cell-surface. Mechanistically, this involves signal-processing through a complex network of molecular interactions that govern transcription factor activity patterns. Computer simulations of the information flow through this network could help predict cellular responses in health and disease. Here we develop a recurrent neural network framework constrained by prior knowledge of the signaling network with ligand-concentrations as input and transcription factor-activity as output. Applied to synthetic data, it predicts unseen test-data (Pearson correlation r = 0.98) and the effects of gene knockouts (r = 0.8). We stimulate macrophages with 59 different ligands, with and without the addition of lipopolysaccharide, and collect transcriptomics data. The framework predicts this data under cross-validation (r = 0.8) and knockout simulations suggest a role for RIPK1 in modulating the lipopolysaccharide response. This work demonstrates the feasibility of genome-scale simulations of intracellular signaling.", "doi": "10.1038/s41467-022-30684-y", "pmid": "35654811", "labels": {"Avlant Nilsson": null, "DDLS Fellow": null}, "xrefs": [{"db": "pmc", "key": "PMC9163072"}, {"db": "pii", "key": "10.1038/s41467-022-30684-y"}], "notes": [], "created": "2025-03-20T11:09:57.547Z", "modified": "2025-03-21T13:18:02.146Z"}]}