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...

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Veröffentlicht in:	PLoS computational biology Jg. 21; H. 6; S. e1013133
Hauptverfasser:	Huang, Jiawei, Du, Yuxuan, Kelly, Kevin R., Lv, Jinchi, Fan, Yingying, Zhong, Jiang F., Sun, Fengzhu
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	United States Public Library of Science 04.06.2025 Public Library of Science (PLoS)
Schlagworte:	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
Online-Zugang:	Volltext
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