ReviewAnnual review of biomedical data science2024
Mapping the Multiscale Proteomic Organization of Cellular and Disease Phenotypes.
Review in Annual review of biomedical data science, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
8 citing papers in PubMed.
- Human genetics across levels of biological organization.Nature reviews. Genetics · 2026Review
- Technologies to measure and modulate protein subcellular localization.Nature reviews. Molecular cell biology · 2026Review
- Subcellular localization as a driver of protein function.Nature reviews. Molecular cell biology · 2026Review
- Cell Mapping Toolkit: an end-to-end pipeline for mapping subcellular organization.Bioinformatics (Oxford, England) · 2025Article
- The Neuroimmune Axis in Gastrointestinal Disorders - An Underrecognized Problem.Current gastroenterology reports · 2025Review
- A Primer on Proteomic Characterization of Intercellular Communication in a Virus Microenvironment.Molecular & cellular proteomics : MCP · 2025Review
- How to build the virtual cell with artificial intelligence: Priorities and opportunities.Cell · 2024Article
- How to Build the Virtual Cell with Artificial Intelligence: Priorities and Opportunities.ArXiv · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
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.
Indexed as
Identifiers
What Socratic holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.