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Addressing challenges in transcriptome annotation and cell-type heterogeneity through integration of omics datasets and computational deconvolution

(2020)
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MLA
Avila Cobos, Francisco. Addressing Challenges in Transcriptome Annotation and Cell-Type Heterogeneity through Integration of Omics Datasets and Computational Deconvolution. Universiteit Gent. Faculteit Geneeskunde en Gezondheidswetenschappen, 2020.
APA
Avila Cobos, F. (2020). Addressing challenges in transcriptome annotation and cell-type heterogeneity through integration of omics datasets and computational deconvolution. Universiteit Gent. Faculteit Geneeskunde en Gezondheidswetenschappen.
Chicago author-date
Avila Cobos, Francisco. 2020. “Addressing Challenges in Transcriptome Annotation and Cell-Type Heterogeneity through Integration of Omics Datasets and Computational Deconvolution.” Universiteit Gent. Faculteit Geneeskunde en Gezondheidswetenschappen.
Chicago author-date (all authors)
Avila Cobos, Francisco. 2020. “Addressing Challenges in Transcriptome Annotation and Cell-Type Heterogeneity through Integration of Omics Datasets and Computational Deconvolution.” Universiteit Gent. Faculteit Geneeskunde en Gezondheidswetenschappen.
Vancouver
1.
Avila Cobos F. Addressing challenges in transcriptome annotation and cell-type heterogeneity through integration of omics datasets and computational deconvolution. Universiteit Gent. Faculteit Geneeskunde en Gezondheidswetenschappen; 2020.
IEEE
[1]
F. Avila Cobos, “Addressing challenges in transcriptome annotation and cell-type heterogeneity through integration of omics datasets and computational deconvolution,” Universiteit Gent. Faculteit Geneeskunde en Gezondheidswetenschappen, 2020.
@phdthesis{8664638,
  author       = {{Avila Cobos, Francisco}},
  language     = {{eng}},
  pages        = {{178}},
  publisher    = {{Universiteit Gent. Faculteit Geneeskunde en Gezondheidswetenschappen}},
  school       = {{Ghent University}},
  title        = {{Addressing challenges in transcriptome annotation and cell-type heterogeneity through integration of omics datasets and computational deconvolution}},
  year         = {{2020}},
}