DeepDeconUQ estimates malignant cell fraction prediction intervals in bulk RNA-seq tissue

Accurate estimation of malignant cell fractions in tissues plays a critical role in cancer diagnosis, prognosis, and subsequent treatment decisions. However, most currently available methods provide only point estimates, neglecting the quantification of uncertainties, which is essential for both cli...

Celý popis

Uložené v:

Podrobná bibliografia
Vydané v:	PLoS computational biology Ročník 21; číslo 6; s. e1013133
Hlavní autori:	Huang, Jiawei, Du, Yuxuan, Kelly, Kevin R., Lv, Jinchi, Fan, Yingying, Zhong, Jiang F., Sun, Fengzhu
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	United States Public Library of Science 04.06.2025 Public Library of Science (PLoS)
Predmet:	Accuracy Artificial neural networks Biology and Life Sciences Calibration Cancer Computational Biology - methods Computer and Information Sciences Conformity Datasets Deep Learning Estimates Gene expression Gene sequencing Genetic aspects Humans Intervals Lower bounds Medical research Medicine and Health Sciences Methods Neoplasms - genetics Neoplasms - pathology Neural networks Neural Networks, Computer Performance evaluation Predictions Quantiles Research and analysis methods Ribonucleic acid RNA RNA sequencing RNA-Seq - methods Sequence Analysis, RNA - methods Single-Cell Analysis - methods Statistical analysis Uncertainty Validity United States
ISSN:	1553-7358, 1553-734X, 1553-7358
On-line prístup:	Získať plný text
Tagy:	Pridať tag Žiadne tagy, Buďte prvý, kto otaguje tento záznam!

Buďte prvý, kto okomentuje tento záznam!