The imputation performance of CancerSD on the gastric cancer dataset (STAD).

The imputation performance of CancerSD on the gastric cancer dataset (STAD).

<p><b>(a)</b> Sample clustering under different scenarios.<b>(b)</b> Similarity between the original samples and the samples after simulating missingness and imputation.<b>(c)</b> Differentially expressed genes are obtained separately from the original mRNA...

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Bibliographic Details
Main Author:	Yongqi Bu (20477564) (author)
Other Authors:	Jiaxuan Liang (12633000) (author), Zhen Li (49109) (author), Jianbo Wang (184708) (author), Jun Wang (5906) (author), Guoxian Yu (3422159) (author)
Published:	2024
Subjects:	Genetics Biotechnology Cancer Science Policy Infectious Diseases Virology Biological Sciences not elsewhere classified Information Systems not elsewhere classified level contrastive loss independent predictive factor gives accurate diagnosis effectively transferring knowledge flexible integrative model benchmark datasets show limited clinical samples collecting sufficient samples missingness </ p integrated cancersd score omics data fusion sufficient samples omics data external datasets diagnostic model clinical applications complete data reconstruction tasks precise treatment learning framework incomplete multi imputed ones high authenticity good interpretability existing multi complete multi cancer subtypes also maintains
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