Evidence map›Paper›PMID 40502087›Full record

ArticlebioRxiv : the preprint server for biology2025

CCC-GPU: A graphics processing unit (GPU)-accelerated nonlinear correlation coefficient for large-scale transcriptomic analyses.

Haoyu Zhang, Kevin Fotso, Marc Subirana-Granés, Milton Pividori

Abstract readPreprint
In one paragraph

Article in bioRxiv : the preprint server for biology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0cells of the map it votes in
0citing 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

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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

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

4 authors.

Haoyu ZhangDepartment of Biomedical Informatics, University of Colorado Anschutz Medical Campus, Aurora, CO, USA.ORCID 0009-0005-6025-0217
Kevin FotsoOffice of Information Technology, University of Colorado Anschutz Medical Campus, Aurora, CO, USA.ORCID 0009-0002-2196-6157
Marc Subirana-GranésDepartment of Biomedical Informatics, University of Colorado Anschutz Medical Campus, Aurora, CO, USA.ORCID 0000-0003-3934-839X
Milton PividoriDepartment of Biomedical Informatics, University of Colorado School of Medicine, Aurora, CO 80045, USA; Colorado Center for Personalized Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO, USA.ORCID 0000-0002-3035-4403

Funding

Novel computational strategies to deconvolute co-occurring conditions in Down syndromeR01HD109765 · NICHD · UNIVERSITY OF COLORADO DENVER · PI James Christopher Costello, Casey S Greene · 2022 to 2026
$4.1M
Computational approaches to characterize heterogeneity and improve risk stratification in complex disease phenotypesR00HG011898 · NHGRI · UNIVERSITY OF COLORADO DENVER · PI PIVIDORI, MILTON · 2023 to 2025
$747k
Computational approaches to characterize heterogeneity and improve risk stratification in complex disease phenotypesK99HG011898 · NHGRI · UNIVERSITY OF PENNSYLVANIA · PI PIVIDORI, MILTON · 2022 to 2022
$121k
NHGRI NIH HHS K99 HG011898NHGRI NIH HHS R00 HG011898NICHD NIH HHS R01 HD109765
6 · The paper itself

Abstract

Motivation: Identifying meaningful patterns in complex biological data necessitates correlation coefficients capable of capturing diverse relationship types beyond simple linearity. Furthermore, efficient computational tools are crucial for handling the ever-increasing scale of biological datasets. Results: We introduce CCC-GPU, a high-performance, GPU-accelerated implementation of the Clustermatch Correlation Coefficient (CCC). CCC-GPU computes correlation coefficients for mixed data types, effectively detects nonlinear relationships, and offers significant speed improvements over its predecessor. Availability and Implementation: CCC-GPU is openly available on GitHub (https://github.com/pivlab/ccc-gpu) and distributed under the BSD-2-Clause Plus Patent License.

Identifiers

PMID40502087
PMCPMC12157546

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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.