ArticleNature biotechnology2021
Learning on knowledge graph dynamics provides an early warning of impactful research.
Article in Nature biotechnology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 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
14 citing papers in PubMed, 61 citations in OpenAlex.
- Prediction of transformative breakthroughs in biomedical research.bioRxiv : the preprint server for biology · 2025Article
- Knowledge Graph for Methane Selective Conversion: Revisiting and Predicting Product Selectivity and Methane Conversion.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Quantifying the impact of biobanks and cohort studies.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Limited diffusion of scientific knowledge forecasts collapse.Nature human behaviour · 2025Article
- Parsing 20 Years of Public Data by AI Maps Trends in Proteomics and Forecasts Technology.Journal of proteome research · 2024Review
- Impact of medical technologies may be predicted using constructed graph bibliometrics.Scientific reports · 2024Article
- Article
- Exploring the application of machine learning to expert evaluation of research impact.PloS one · 2023Article
- A cultural evolutionary theory that explains both gradual and punctuated change.Journal of the Royal Society, Interface · 2022Article
- Deep forecasting of translational impact in medical research.Patterns (New York, N.Y.) · 2022Article
- Scientific X-ray: Scanning and quantifying the idea evolution of scientific publications.PloS one · 2022Article
- Speeding up to keep up: exploring the use of AI in the research process.AI & society · 2022Article
- Cancer nanotechnology: current status and perspectives.Nano convergence · 2021Review
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors at 2 institutions in 1 country.
Funding
Abstract
The scientific ecosystem relies on citation-based metrics that provide only imperfect, inconsistent and easily manipulated measures of research quality. Here we describe DELPHI (Dynamic Early-warning by Learning to Predict High Impact), a framework that provides an early-warning signal for 'impactful' research by autonomously learning high-dimensional relationships among features calculated across time from the scientific literature. We prototype this framework and deduce its performance and scaling properties on time-structured publication graphs from 1980 to 2019 drawn from 42 biotechnology-related journals, including over 7.8 million individual nodes, 201 million relationships and 3.8 billion calculated metrics. We demonstrate the framework's performance by correctly identifying 19/20 seminal biotechnologies from 1980 to 2014 via a blinded retrospective study and provide 50 research papers from 2018 that DELPHI predicts will be in the top 5% of time-rescaled node centrality in the future. We propose DELPHI as a tool to aid in the construction of diversified, impact-optimized funding portfolios.
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.