ENA 2022 Poster 273

Comparison of Biomarker Selection Method in High-Dimensional Genomic Data

Yueying Wang, Sheng Guo

Biomarker discovery based on genomic data provides deep insight into drug mechanisms and efficacy prediction and is indispensable in targeted therapeutics development.

However, genomic data is complex and high-dimensional, finding the right signature gene(s) from thousands of genes with highly fluctuating and often closely related expression is challenging. Many signature gene selection algorithms have been developed for this purpose.

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• The performance comparison of three recent algorithms in several large in vitro cellular assay
  • Stable Iterative Variable Selection (Mahmoudian et al., 2021)
  • Precision Lasso (Wang et al., 2019)
  • Whitening Lasso (Zhu et al., 2021)

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