Health AI Central
Peer-Reviewed Publication
Proc Natl Acad Sci U S A2025;122(34):e2509860122.August 26, 2025Journal Article

Efficiently quantifying dependence in massive scientific datasets using InterDependence Scores.

Adityanarayanan Radhakrishnan1,2, Yajit Jain1, Caroline Uhler1,3, Eric S Lander1,4,5
1Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA 02142.
2Department of Mathematics, Massachusetts Institute of Technology, Cambridge, MA 02139.
3Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA 02139.
4Department of Biology, Massachusetts Institute of Technology, Cambridge, MA 02139.
5Department of Systems Biology, Harvard Medical School, Boston, MA 02115.
PubMed PMC DOI PDF

Abstract

Large-scale scientific datasets today contain tens of thousands of random variables across millions of samples (for example, the RNA expression levels of 20,000 protein-coding genes across 30 million single cells). Being able to quantify dependencies between these variables would help us discover novel relationships between variables of interest. Simple measures of dependence, such as Pearson corr…

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