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Table 1 Different missing values replacement methods.

From: Comparative analysis of missing value imputation methods to improve clustering and interpretation of microarray experiments

Methods

Author

Availability

Language

Used

Year

K-Nearest Neighbors (kNN)

Troyanskaya O.

Y

C

Y

2001

Bayesian Pricipal Component Analysis (BPCA)

Oba S.

Y

JAVA

Y

2003

Row Mean 1

Bø T.H.

Y

JAVA

Y

2004

EM_gene 1

Bø T.H.

Y

JAVA

Y

2004

EM_array 1

Bø T.H.

Y

JAVA

Y

2004

LSI_gene 1

Bø T.H.

Y

JAVA

Y

2004

LSI_array 1

Bø T.H.

Y

JAVA

Y

2004

LSI_combined 1

Bø T.H.

Y

JAVA

Y

2004

LSI_adaptative 1

Bø T.H.

Y

JAVA

Y

2004

Sequential KNN (SkNN)

Kim K.

Y

R

Y

2004

Local Least Square Impute2 (LLSI)

Kim H.

Y

MATLAB

Y

2005

Row Average 2

Kim H.

Y

MATLAB

Y

2005

Linear model based Imputation (LinImp)

Scheel I

Y

R

N

2005

FAR, Factor Analysis Regression (FAR)

Feten.

N

-

N

2005

Ordinary Least Square Impute (OLSI)

Nguyen D.V.

N

-

N

2004

Support Vector Regression (SVR)

Wang X.

Y

C++

N

2006

Gaussian Mixture Clustering (GMC)

Ouyang M.

On demand

MATLAB

N

2004

Singular Value Decomposition (SVD)

Troyanskaya O.

N

C

N

2001

ghmm

Schielp, A

Y

 

N

2003

Collateral Missing Value Estimation (CMVE)

Sehgal M.

On demand

MATLAB

N

2005

GO-based imputation

Tuikkala

N

-

N

2005

LinCmb

Jörnsten, R

On demand

MATLAB

N

2005

Integrative Missing value Estimation (iMISS)

Hu, J

Y

C++

N

2006

Projection Onto convex sets (POCS)

Gan, X

N

-

N

2006

Iterative kNN

Bras

N

-

N

2007

  1. Is given the name of the methods, the authors, its availability, if we have used it (Y) or not (N) and the publication year.
  2. 1 Package Bø T.H.
  3. 2 Package Kim H.