The imputation performance of CancerSD on STAD dataset with missingness occuring in methylation data.

The imputation performance of CancerSD on STAD dataset with missingness occuring in methylation data.

<p><b>(a)</b> Mean Absolute Error (MAE) between original and imputed methylation data in the testing set at different missing rates, and corresponding Root Mean Square Error (RMSE) at each rate.<b>(b)</b> Similarity between original and imputed methylation data under va...

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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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