Evidence map›Paper›PMID 35969737›Full record

ArticleProceedings of the National Academy of Sciences of the United States of America2022

Model-free prediction test with application to genomics data.

Zhanrui Cai, Jing Lei, Kathryn Roeder

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Proceedings of the National Academy of Sciences of the United States of America, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. AICO: Feature significance tests for supervised learning.Proceedings of the National Academy of Sciences of the United States of America · 2026
    Article
  2. Article
  3. Asymptotic Distribution-Free Independence Test for High Dimension Data.Journal of the American Statistical Association · 2024
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Zhanrui CaiDepartment of Statistics, Iowa State University, Ames, IA 50011.
Jing LeiDepartment of Statistics and Data Science, Carnegie Mellon University, Pittsburgh, PA 15213.ORCID 0000-0003-3104-9387
Kathryn RoederDepartment of Statistics and Data Science, Carnegie Mellon University, Pittsburgh, PA 15213.ORCID 0000-0002-8869-6254

Funding

Methods for single-cell CRISPR screens and multiomic data: constructing powerful well-calibrated tests, circumventing unmeasured confounding, and accounting for denoising and imputationR01MH123184 · NIMH · CARNEGIE-MELLON UNIVERSITY · PI KATHRYN M ROEDER · 2020 to 2026
$3.8M
NIMH NIH HHS R01 MH123184
6 · The paper itself

Abstract

Testing the significance of predictors in a regression model is one of the most important topics in statistics. This problem is especially difficult without any parametric assumptions on the data. This paper aims to test the null hypothesis that given confounding variables

Indexed as

GenomicsMachine LearningAlgorithmsRegression AnalysisTranscriptomeCITE-seq datamachine learningprediction testsample splittingspatially variable genes

Identifiers

PMID35969737
PMCPMC9407618

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

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.