{"entity": "journal", "iuid": "1073442d100445e2b6ff30ced0e0abb4", "timestamp": "2026-08-22T06:58:52.649Z", "links": {"self": {"href": "https://publications-affiliated.scilifelab.se/journal/Annu%20Rev%20Biomed%20Data%20Sci.json"}, "display": {"href": "https://publications-affiliated.scilifelab.se/journal/Annu%20Rev%20Biomed%20Data%20Sci"}}, "title": "Annu Rev Biomed Data Sci", "issn": "2574-3414", "issn-l": null, "publications_count": 2, "publications": [{"entity": "publication", "iuid": "a784af8351a4414a942ce743befb14e3", "links": {"self": {"href": "https://publications-affiliated.scilifelab.se/publication/a784af8351a4414a942ce743befb14e3.json"}, "display": {"href": "https://publications-affiliated.scilifelab.se/publication/a784af8351a4414a942ce743befb14e3"}}, "title": "Mapping the Multiscale Proteomic Organization of Cellular and Disease Phenotypes.", "authors": [{"family": "Cesnik", "given": "Anthony", "initials": "A"}, {"family": "Schaffer", "given": "Leah V", "initials": "LV"}, {"family": "Gaur", "given": "Ishan", "initials": "I"}, {"family": "Jain", "given": "Mayank", "initials": "M"}, {"family": "Ideker", "given": "Trey", "initials": "T"}, {"family": "Lundberg", "given": "Emma", "initials": "E"}], "type": "journal article", "published": "2024-08-00", "journal": {"title": "Annu Rev Biomed Data Sci", "issn": "2574-3414", "volume": "7", "issue": "1", "pages": "369-389", "issn-l": null}, "abstract": "While the primary sequences of human proteins have been cataloged for over a decade, determining how these are organized into a dynamic collection of multiprotein assemblies, with structures and functions spanning biological scales, is an ongoing venture. Systematic and data-driven analyses of these higher-order structures are emerging, facilitating the discovery and understanding of cellular phenotypes. At present, knowledge of protein localization and function has been primarily derived from manual annotation and curation in resources such as the Gene Ontology, which are biased toward richly annotated genes in the literature. Here, we envision a future powered by data-driven mapping of protein assemblies. These maps can capture and decode cellular functions through the integration of protein expression, localization, and interaction data across length scales and timescales. In this review, we focus on progress toward constructing integrated cell maps that accelerate the life sciences and translational research.", "doi": "10.1146/annurev-biodatasci-102423-113534", "pmid": "38748859", "labels": [], "xrefs": [{"db": "mid", "key": "NIHMS2004462"}, {"db": "pmc", "key": "PMC11343683"}], "notes": [], "created": "2026-08-20T12:02:54.366Z", "modified": "2026-08-20T12:02:54.431Z"}, {"entity": "publication", "iuid": "d66c687e31444c63a2d12a97dc967cef", "links": {"self": {"href": "https://publications-affiliated.scilifelab.se/publication/d66c687e31444c63a2d12a97dc967cef.json"}, "display": {"href": "https://publications-affiliated.scilifelab.se/publication/d66c687e31444c63a2d12a97dc967cef"}}, "title": "Functional Characterization of Genetic Variant Effects on Expression.", "authors": [{"family": "Flynn", "given": "Elise D", "initials": "ED"}, {"family": "Lappalainen", "given": "Tuuli", "initials": "T"}], "type": "journal article", "published": "2022-08-10", "journal": {"title": "Annu Rev Biomed Data Sci", "issn": "2574-3414", "volume": "5", "pages": "119-139", "issn-l": null}, "abstract": "Thousands of common genetic variants in the human population have been associated with disease risk and phenotypic variation by genome-wide association studies (GWAS). However, the majority of GWAS variants fall into noncoding regions of the genome, complicating our understanding of their regulatory functions, and few molecular mechanisms of GWAS variant effects have been clearly elucidated. Here, we set out to review genetic variant effects, focusing on expression quantitative trait loci (eQTLs), including their utility in interpreting GWAS variant mechanisms. We discuss the interrelated challenges and opportunities for eQTL analysis, covering determining causal variants, elucidating molecular mechanisms of action, and understanding context variability. Addressing these questions can enable better functional characterization of disease-associated loci and provide insights into fundamental biological questions of the noncoding genetic regulatory code and its control of gene expression.", "doi": "10.1146/annurev-biodatasci-122120-010010", "pmid": "35483347", "labels": [], "xrefs": [], "notes": [], "created": "2026-08-20T12:02:56.232Z", "modified": "2026-08-20T12:02:56.279Z"}], "created": "2026-08-20T12:02:54.397Z", "modified": "2026-08-20T12:02:54.397Z"}