Praxis-BGM: clustering of omics data using semi-supervised transfer learning for Gaussian mixture models via natural-gradient variational inference.
- DOI
- 10.1093/bioinformatics/btag395
- Published
- 2026 Jun 1
- Container
- Bioinformatics (Oxford, England)
- Publisher
- Not recorded
- Open access
- yes
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Cite this work
BibTeX
@article{allodium:10.1093/bioinformatics/btag395,
title = {Praxis-BGM: clustering of omics data using semi-supervised transfer learning for Gaussian mixture models via natural-gradient variational inference.},
author = {Jia Q and Goodrich JA and Conti DV},
year = {2026},
journal = {Bioinformatics (Oxford, England)},
doi = {10.1093/bioinformatics/btag395},
url = {https://doi.org/10.1093/bioinformatics/btag395}
}RIS
TY - JOUR TI - Praxis-BGM: clustering of omics data using semi-supervised transfer learning for Gaussian mixture models via natural-gradient variational inference. AU - Jia Q AU - Goodrich JA AU - Conti DV PY - 2026 JO - Bioinformatics (Oxford, England) DO - 10.1093/bioinformatics/btag395 UR - https://doi.org/10.1093/bioinformatics/btag395 ER -
APA
Q, J., JA, G., & DV, C. (2026). Praxis-BGM: clustering of omics data using semi-supervised transfer learning for Gaussian mixture models via natural-gradient variational inference.. Bioinformatics (Oxford, England). https://doi.org/10.1093/bioinformatics/btag395
Source records
- pubmed · retrieved 2026-09-25T03:05:13.030Z