ReviewJournal of proteome research2024
Parsing 20 Years of Public Data by AI Maps Trends in Proteomics and Forecasts Technology.
Review in Journal of proteome research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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
1 citing paper in PubMed.
- Complexoform-restricted covalent TRMT112 ligands that allosterically agonize METTL5.Nature chemical biology · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
The trends of the last 20 years in biotechnology were revealed using artificial intelligence and natural language processing (NLP) of publicly available data. Implementing this "science-of-science" approach, we capture convergent trends in the field of proteomics in both technology development and application across the phylogenetic tree of life. With major gaps in our knowledge about protein composition, structure, and location over time, we report trends in persistent, popular approaches and emerging technologies across 94 ideas from a corpus of 29 journals in PubMed over two decades. New metrics for clusters of these ideas reveal the progression and popularity of emerging approaches like single-cell, spatial, compositional, and chemical proteomics designed to better capture protein-level chemistry and biology. This analysis of the proteomics literature with advanced analytic tools quantifies the Rate of Rise for a next generation of technologies to better define, quantify, and visualize the multiple dimensions of the proteome that will transform our ability to measure and understand proteins in the coming decade.
Indexed as
Identifiers
What OpenQuestion 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 OpenQuestion graph.