Improved KNN Imputation for Missing Values in Gene Expression Data

Phimmarin Keerin, Tossapon Boongoen (Corresponding Author)

Research output: Contribution to journalArticlepeer-review

10 Citations (SciVal)
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Abstract

The problem of missing values has long been studied by researchers working in areas of data science and bioinformatics, especially the analysis of gene expression data that facilitates an early detection of cancer. Many attempts show improvements made by excluding samples with missing information from the analysis process, while others have tried to fill the gaps with possible values. While the former is simple, the latter safeguards information loss. For that, a neighbour-based (KNN) approach has proven more effective than other global estimators. The paper extends this further by introducing a new summarization method to the KNN model. It is the first study that applies the concept of ordered weighted averaging (OWA) operator to such a problem context. In particular, two variations of OWA aggregation are proposed and evaluated against their baseline and other neighbor-based models. Using different ratios of missing values from 1%–20% and a set of six published gene expression datasets, the experimental results suggest that new methods usually provide more accurate estimates than those compared methods. Specific to the missing rates of 5% and 20%, the best NRMSE scores as averages across datasets is 0.65 and 0.69, while the highest measures obtained by existing techniques included in this study are 0.80 and 0.84, respectively.
Original languageEnglish
Pages (from-to)4009-4025
Number of pages17
JournalComputers, Materials and Continua
Volume70
Issue number2
DOIs
Publication statusPublished - 27 Sept 2021
Externally publishedYes

Keywords

  • Gene expression
  • Missing value
  • Imputation
  • KNN
  • OWA operator

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