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Sequential local least squares imputation estimating missing value of microarray data
- Source :
- Computers in Biology and Medicine. 38:1112-1120
- Publication Year :
- 2008
- Publisher :
- Elsevier BV, 2008.
-
Abstract
- Missing values in microarray data can significantly affect subsequent analysis, thus it is important to estimate these missing values accurately. In this paper, a sequential local least squares imputation (SLLSimpute) method is proposed to solve this problem. It estimates missing values sequentially from the gene containing the fewest missing values and partially utilizes these estimated values. In addition, an automatic parameter selection algorithm, which can generate an appropriate number of neighboring genes for each target gene, is presented for parameter estimation. Experimental results confirmed that SLLSimpute method exhibited better estimation ability compared with other currently used imputation methods.
- Subjects :
- Estimation theory
business.industry
Microarray analysis techniques
Health Informatics
Pattern recognition
Models, Theoretical
Missing data
Quantitative Biology::Genomics
Computer Science Applications
Artificial intelligence
Imputation (statistics)
Least-Squares Analysis
Target gene
business
Selection algorithm
Oligonucleotide Array Sequence Analysis
Mathematics
Subjects
Details
- ISSN :
- 00104825
- Volume :
- 38
- Database :
- OpenAIRE
- Journal :
- Computers in Biology and Medicine
- Accession number :
- edsair.doi.dedup.....51522b6633f5fa1023d09707a3cdd034