{"entity": "journal", "iuid": "9b3efc3ed07f40fc8bb65a889eed6f89", "timestamp": "2026-08-22T06:51:35.336Z", "links": {"self": {"href": "https://publications-affiliated.scilifelab.se/journal/Trends%20Cell%20Biol.json"}, "display": {"href": "https://publications-affiliated.scilifelab.se/journal/Trends%20Cell%20Biol"}}, "title": "Trends Cell Biol", "issn": "1879-3088", "issn-l": null, "publications_count": 1, "publications": [{"entity": "publication", "iuid": "bbc7688f43ab40e68469fb34d352b357", "links": {"self": {"href": "https://publications-affiliated.scilifelab.se/publication/bbc7688f43ab40e68469fb34d352b357.json"}, "display": {"href": "https://publications-affiliated.scilifelab.se/publication/bbc7688f43ab40e68469fb34d352b357"}}, "title": "Data-driven bioinformatics to disentangle cells within a tissue microenvironment.", "authors": [{"family": "Tegner", "given": "Jesper N", "initials": "JN"}, {"family": "Gomez-Cabrero", "given": "David", "initials": "D"}], "type": "journal article", "published": "2022-06-00", "journal": {"title": "Trends Cell Biol", "issn": "1879-3088", "volume": "32", "issue": "6", "pages": "467-469", "issn-l": null}, "abstract": "Molecular profiling of clinical tissue samples is at the core of precision medicine. Yet, to elucidate the contribution of mixed cell types and detect changes in cell populations in response to infections or drugs is challenging. Recent advances using machine learning promise to learn explanatory models directly from data.", "doi": "10.1016/j.tcb.2022.03.009", "pmid": "35430125", "labels": [], "xrefs": [{"db": "pii", "key": "S0962-8924(22)00081-2"}], "notes": [], "created": "2026-08-21T11:29:45.908Z", "modified": "2026-08-21T11:29:45.966Z"}], "created": "2026-08-21T11:29:45.927Z", "modified": "2026-08-21T11:29:45.927Z"}