ArticleNature communications2023
Lacking mechanistic disease definitions and corresponding association data hamper progress in network medicine and beyond.
Article in Nature communications, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 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
7 citing papers in PubMed.
- Precision Medicine Through Network Language: Integrating Clinical Insight and Data Expertise.Genes · 2026Review
- Disease Network-Based Approaches to Study Comorbidity in Heart Failure: Current State and Future Perspectives.Current heart failure reports · 2024Review
- A Multi-Dimensional Approach to Map Disease Relationships Challenges Classical Disease Views.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2024Article
- Optimized network based natural language processing approach to reveal disease comorbidities in COVID-19.Scientific reports · 2024Article
- Synovial fluid extracellular vesicles as arthritis biomarkers: the added value of lipid-profiling and integrated omics.Extracellular vesicles and circulating nucleic acids · 2024Review
- Drugst.One - A plug-and-play solution for online systems medicine and network-based drug repurposing.ArXiv · 2023Article
- Lacking mechanistic disease definitions and corresponding association data hamper progress in network medicine and beyond.Nature communications · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
13 authors.
Funding
Abstract
A long-term objective of network medicine is to replace our current, mainly phenotype-based disease definitions by subtypes of health conditions corresponding to distinct pathomechanisms. For this, molecular and health data are modeled as networks and are mined for pathomechanisms. However, many such studies rely on large-scale disease association data where diseases are annotated using the very phenotype-based disease definitions the network medicine field aims to overcome. This raises the question to which extent the biases mechanistically inadequate disease annotations introduce in disease association data distort the results of studies which use such data for pathomechanism mining. We address this question using global- and local-scale analyses of networks constructed from disease association data of various types. Our results indicate that large-scale disease association data should be used with care for pathomechanism mining and that analyses of such data should be accompanied by close-up analyses of molecular data for well-characterized patient cohorts.
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.