ArticleeLife2019
Discovering and deciphering relationships across disparate data modalities.
Article in eLife, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 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
10 citing papers in PubMed.
- Universally Consistent K-Sample Tests via Dependence Measures.Statistics & probability letters · 2025Article
- Is Pearson's correlation coefficient enough for functional connectivity in fMRI?Imaging neuroscience (Cambridge, Mass.) · 2025Article
- Kernel-based joint independence tests for multivariate stationary and non-stationary time series.Royal Society open science · 2023Article
- Wavelet coherence phase analysis decodes the universal switching mechanism of Ras GTPase superfamily.iScience · 2023Article
- The Chi-Square Test of Distance Correlation.Journal of computational and graphical statistics : a joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America · 2022Article
- Evaluation of a personalized functional near infra-red optical tomography workflow using maximum entropy on the mean.Human brain mapping · 2021Article
- Eliminating accidental deviations to minimize generalization error and maximize replicability: Applications in connectomics and genomics.PLoS computational biology · 2021Article
- Functional connectivity of EEG is subject-specific, associated with phenotype, and different from fMRI.NeuroImage · 2020Article
- Toward Community-Driven Big Open Brain Science: Open Big Data and Tools for Structure, Function, and Genetics.Annual review of neuroscience · 2020Review
- Analysis of brain subnetworks within the context of their whole-brain networks.Human brain mapping · 2019Article
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
Understanding the relationships between different properties of data, such as whether a genome or connectome has information about disease status, is increasingly important. While existing approaches can test whether two properties are related, they may require unfeasibly large sample sizes and often are not interpretable. Our approach, 'Multiscale Graph Correlation' (MGC), is a dependence test that juxtaposes disparate data science techniques, including k-nearest neighbors, kernel methods, and multiscale analysis. Other methods may require double or triple the number of samples to achieve the same statistical power as MGC in a benchmark suite including high-dimensional and nonlinear relationships, with dimensionality ranging from 1 to 1000. Moreover, MGC uniquely characterizes the latent geometry underlying the relationship, while maintaining computational efficiency. In real data, including brain imaging and cancer genetics, MGC detects the presence of a dependency and provides guidance for the next experiments to conduct.
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.