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

Title:
CCC-GPU: a graphics processing unit (GPU)-accelerated nonlinear correlation coefficient for large-scale transcriptomic analyses.
Authors:
Zhang H; Department of Biomedical Informatics, University of Colorado Anschutz, Aurora, Colorado, 80045, United States., Fotso KT; Information Strategy and Services, University of Colorado Anschutz, Aurora, Colorado, 80045, United States., Subirana-Granés M; Department of Biomedical Informatics, University of Colorado Anschutz, Aurora, Colorado, 80045, United States., Pividori M; Department of Biomedical Informatics, University of Colorado Anschutz, Aurora, Colorado, 80045, United States.; Colorado Center for Personalized Medicine, University of Colorado Anschutz, Aurora, Colorado, 80045, United States.
Source:
Bioinformatics (Oxford, England) [Bioinformatics] 2026 Feb 28; Vol. 42 (3).
Publication Type:
Journal Article
Language:
English
Journal Info:
Publisher: Oxford University Press Country of Publication: England NLM ID: 9808944 Publication Model: Print Cited Medium: Internet ISSN: 1367-4811 (Electronic) Linking ISSN: 13674803 NLM ISO Abbreviation: Bioinformatics Subsets: MEDLINE
Imprint Name(s):
Original Publication: Oxford : Oxford University Press, c1998-
MeSH Terms:
Gene Expression Profiling*/methods , Computational Biology*/methods , Software* , Computer Graphics* , Transcriptome*, Algorithms
Comments:
Update of: bioRxiv. 2025 Sep 23:2025.06.03.657735. doi: 10.1101/2025.06.03.657735.. (PMID: 40502087)
Grant Information:
R00 HG011898 United States HG NHGRI NIH HHS; R01 HD109765 United States HD NICHD NIH HHS
Entry Date(s):
Date Created: 20260214 Date Completed: 20260312 Latest Revision: 20260314
Update Code:
20260314
PubMed Central ID:
PMC12980328
DOI:
10.1093/bioinformatics/btag068
PMID:
41689182
Database:
MEDLINE

*Further Information*

*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: The source code of CCC-GPU is openly available on GitHub (https://github.com/pivlab/ccc-gpu) and archived on Zenodo (https://doi.org/10.5281/zenodo.18310318), distributed under the BSD-2-Clause Plus Patent License.
(© The Author(s) 2026. Published by Oxford University Press.)*