An Iterative Locally Auto-Weighted Least Squares Method for Microarray Missing Value Estimation

@article{Yu2017AnIL,
  title={An Iterative Locally Auto-Weighted Least Squares Method for Microarray Missing Value Estimation},
  author={Zeng Si Yu and Tianrui Li and Shi-Jinn Horng and Yi Pan and Hongjun Wang and Yunge Jing},
  journal={IEEE Transactions on NanoBioscience},
  year={2017},
  volume={16},
  pages={21-33}
}
Microarray data often contain missing values which significantly affect subsequent analysis. Existing LLSimpute-based imputation methods for dealing with missing data have been shown to be generally efficient. However, all of the LLSimpute-based methods do not consider the different importance of different neighbors of the target gene in the missing value estimation process and treat all the neighbors equally. In this paper, a locally auto-weighted least squares imputation (LAW-LSimpute) method… CONTINUE READING

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