TY - GEN
T1 - A quasi-linear approach for microarray missing value imputation
AU - Cheng, Yu
AU - Wang, Lan
AU - Hu, Jinglu
PY - 2011
Y1 - 2011
N2 - Missing value imputation for microarray data is important for gene expression analysis algorithms, such as clustering, classification and network design. A number of algorithms have been proposed to solve this problem, but most of them are only limited in linear analysis methods, such as including the estimation in the linear combination of other no-missing-value genes. It may result from the fact that microarray data often comprises of huge size of genes with only a small number of observations, and nonlinear regression techniques are prone to overfitting. In this paper, a quasi-linear SVR model is proposed to improve the linear approaches, and it can be explained in a piecewise linear interpolation way. Two real datasets are tested and experimental results show that the quasi-linear approach for missing value imputation outperforms both the linear and nonlinear approaches.
AB - Missing value imputation for microarray data is important for gene expression analysis algorithms, such as clustering, classification and network design. A number of algorithms have been proposed to solve this problem, but most of them are only limited in linear analysis methods, such as including the estimation in the linear combination of other no-missing-value genes. It may result from the fact that microarray data often comprises of huge size of genes with only a small number of observations, and nonlinear regression techniques are prone to overfitting. In this paper, a quasi-linear SVR model is proposed to improve the linear approaches, and it can be explained in a piecewise linear interpolation way. Two real datasets are tested and experimental results show that the quasi-linear approach for missing value imputation outperforms both the linear and nonlinear approaches.
KW - SVR
KW - microarray data
KW - missing value imputation
KW - quasi-linear
UR - http://www.scopus.com/inward/record.url?scp=81855190908&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=81855190908&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-24955-6_28
DO - 10.1007/978-3-642-24955-6_28
M3 - Conference contribution
AN - SCOPUS:81855190908
SN - 9783642249549
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 233
EP - 240
BT - Neural Information Processing - 18th International Conference, ICONIP 2011, Proceedings
T2 - 18th International Conference on Neural Information Processing, ICONIP 2011
Y2 - 13 November 2011 through 17 November 2011
ER -