A deep learning architecture for combining and imputing heterogeneous metabolomics datasets.

Celik S, Can B, Erdogan MA, Cakmak A

DOI: 10.1186/s12859-026-06560-7
Published: 2026 Jul 16
Container: BMC bioinformatics
Publisher: Not recorded
Open access: unknown

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Cite this work

BibTeX

@article{allodium:10.1186/s12859-026-06560-7,
  title = {A deep learning architecture for combining and imputing heterogeneous metabolomics datasets.},
  author = {Celik S and Can B and Erdogan MA and Cakmak A},
  year = {2026},
  journal = {BMC bioinformatics},
  doi = {10.1186/s12859-026-06560-7},
  url = {https://doi.org/10.1186/s12859-026-06560-7}
}

RIS

TY  - JOUR
TI  - A deep learning architecture for combining and imputing heterogeneous metabolomics datasets.
AU  - Celik S
AU  - Can B
AU  - Erdogan MA
AU  - Cakmak A
PY  - 2026
JO  - BMC bioinformatics
DO  - 10.1186/s12859-026-06560-7
UR  - https://doi.org/10.1186/s12859-026-06560-7
ER  -

APA

S, C., B, C., MA, E., & A, C. (2026). A deep learning architecture for combining and imputing heterogeneous metabolomics datasets.. BMC bioinformatics. https://doi.org/10.1186/s12859-026-06560-7

Source records

pubmed · retrieved 2026-09-25T23:56:58.284Z