Editorial Note: Machine learning driven optimization of compressive strength of 3D printed bio polymer composite material

Abstract
Genes, proteins, chemicals, diseases, species, mutations and cell lines named across the full text — each resolved to its canonical identifier and authoritative record.
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsMachine Learning in Materials Science · Additive Manufacturing and 3D Printing Technologies · Composite Material Mechanics
The PLOS One Editors issue this Editorial Note to inform readers of concerns regarding compliance with PLOS Authorship policy for this article [1]. We regret that the issues were not addressed prior to the article’s publication.
The reference list from the paper itself. Each links out to its DOI / PubMed record.
- 1Jayaram RS, Saravanamuthukumar P, Abdullah AB, Krishnamoorthy R, Kunar S, Yong X, et al. Machine learning driven optimization of compressive strength of 3D printed bio polymer composite material. P Lo S One. 2025;20(8):e 0330625. doi: 10.1371/journal.pone.0330625 40875778 PMC 12393749 · doi ↗ · pubmed ↗
