Home ›
Entities
› academia
› gEM/GANN: A multivariate computational strategy for auto‐characterizing relationships between cellular and clinical phenotypes and predicting disease progression time using high‐dimensional flow cytometry data
gEM/GANN: A multivariate computational strategy for auto‐characterizing relationships between cellular and clinical phenotypes and predicting disease progression time using high‐dimensional flow cytometry data
Research article (Cytometry Part A, 2015) · cited 14× · AI/ML
gEM/GANN: A multivariate computational strategy for auto‐characterizing relationships between cellular and clinical phenotypes and predicting disease progression time using high‐dimensional flow cytometry data
Summary
gEM/GANN: A multivariate computational strategy for auto‐characterizing relationships between cellular and clinical phenotypes and predicting disease progression time using high‐dimensional flow cytometry data is a scholarly article[1].
Key Facts
gEM/GANN: A multivariate computational strategy for auto‐characterizing relationships between cellular and clinical phenotypes and predicting disease progression time using high‐dimensional flow cytometry data's instance of is recorded as scholarly article[2].
References
Programmatic citations — every numbered marker resolves to a verifiable graph row below.
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). gEM/GANN: A multivariate computational strategy for auto‐characterizing relationships between cellular and clinical phenotypes and predicting disease progression time using high‐dimensional flow cytometry data. Retrieved May 24, 2026, from https://4ort.xyz/entity/gem-gann-a-multivariate-computational-strategy-for-autocharacterizing-relationships-between-cellular-and-clinical-phenot
MLA“gEM/GANN: A multivariate computational strategy for auto‐characterizing relationships between cellular and clinical phenotypes and predicting disease progression time using high‐dimensional flow cytometry data.” 4ort.xyz Knowledge Graph, 4ort.xyz, 24 May. 2026, https://4ort.xyz/entity/gem-gann-a-multivariate-computational-strategy-for-autocharacterizing-relationships-between-cellular-and-clinical-phenot.
BibTeX@misc{4ortxyz_gem-gann-a-multivariate-computational-strategy-for-autocharacterizing-relationships-between-cellular-and-clinical-phenot_2026, author = {{4ort.xyz Knowledge Graph}}, title = {{gEM/GANN: A multivariate computational strategy for auto‐characterizing relationships between cellular and clinical phenotypes and predicting disease progression time using high‐dimensional flow cytometry data}}, year = {2026}, url = {https://4ort.xyz/entity/gem-gann-a-multivariate-computational-strategy-for-autocharacterizing-relationships-between-cellular-and-clinical-phenot}, note = {Accessed: 2026-05-24}}
LLM promptAccording to 4ort.xyz Knowledge Graph (aggregator of Wikidata, Wikipedia, and authoritative open-data sources): gEM/GANN: A multivariate computational strategy for auto‐characterizing relationships between cellular and clinical phenotypes and predicting disease progression time using high‐dimensional flow cytometry data — https://4ort.xyz/entity/gem-gann-a-multivariate-computational-strategy-for-autocharacterizing-relationships-between-cellular-and-clinical-phenot (retrieved 2026-05-24)
