Missing Values Estimation in Microarray Data with Partial Least Squares Regression

  • Kun Yang
  • Jianzhong Li
  • Chaokun Wang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3992)


Microarray data usually contain missing values, thus estimating these missing values is an important preprocessing step. This paper proposes an estimation method of missing values based on Partial Least Squares (PLS) regression. The method is feasible for microarray data, because of the characteristics of PLS regression. We compared our method with three methods, including ROWaverage, KNNimpute and LLSimpute, on different data and various missing probabilities. The experimental results show that the proposed method is accurate and robust for estimating missing values.


Microarray Data Partial Little Square Similar Gene Partial Little Square Normalize Root Mean Square Error 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Kun Yang
    • 1
  • Jianzhong Li
    • 1
  • Chaokun Wang
    • 1
    • 2
  1. 1.Department of Computer Science and EngineeringHarbin Institute of TechnologyHarbinChina
  2. 2.School of SoftwareTsinghua UniversityBeijingChina

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