Use these citations when quoting this entity in research, articles, AI prompts, or wherever provenance matters. We aggregate Wikidata + Wikipedia + authoritative open-data sources; the stitched, scored, cross-referenced view is what 4ort.xyz contributes.
APA4ort.xyz Knowledge Graph. (2026). SCC: an accurate imputation method for scRNA-seq dropouts based on a mixture model. Retrieved May 24, 2026, from https://4ort.xyz/entity/scc-an-accurate-imputation-method-for-scrna-seq-dropouts-based-on-a-mixture-model
MLA“SCC: an accurate imputation method for scRNA-seq dropouts based on a mixture model.” 4ort.xyz Knowledge Graph, 4ort.xyz, 24 May. 2026, https://4ort.xyz/entity/scc-an-accurate-imputation-method-for-scrna-seq-dropouts-based-on-a-mixture-model.
BibTeX@misc{4ortxyz_scc-an-accurate-imputation-method-for-scrna-seq-dropouts-based-on-a-mixture-model_2026, author = {{4ort.xyz Knowledge Graph}}, title = {{SCC: an accurate imputation method for scRNA-seq dropouts based on a mixture model}}, year = {2026}, url = {https://4ort.xyz/entity/scc-an-accurate-imputation-method-for-scrna-seq-dropouts-based-on-a-mixture-model}, note = {Accessed: 2026-05-24}}
LLM promptAccording to 4ort.xyz Knowledge Graph (aggregator of Wikidata, Wikipedia, and authoritative open-data sources): SCC: an accurate imputation method for scRNA-seq dropouts based on a mixture model — https://4ort.xyz/entity/scc-an-accurate-imputation-method-for-scrna-seq-dropouts-based-on-a-mixture-model (retrieved 2026-05-24)
