Feature-specific quantile normalization and feature-specific mean–variance normalization deliver robust bi-directional classification and feature selection performance between microarray and RNAseq data

Feature-specific quantile normalization and feature-specific mean–variance normalization deliver robust bi-directional classification and feature selection performance between microarray and RNAseq data

Abstract Background Cross-platform normalization seeks to minimize technological bias between microarray and RNAseq whole-transcriptome data. Incorporating multiple gene expression platforms permits external validation of experimental findings, and augments training sets for machine learning models....

Πλήρης περιγραφή

Λεπτομέρειες βιβλιογραφικής εγγραφής
Κύριοι συγγραφείς: Daniel Skubleny, Sunita Ghosh, Jennifer Spratlin, Daniel E. Schiller, Gina R. Rayat
Μορφή: Άρθρο
Γλώσσα:English
Έκδοση: BMC 2024-03-01
Σειρά:BMC Bioinformatics
Θέματα:
Cross-platform normalization
Molecular classification
Microarray
RNAseq
Feature selection
Quantile normalization
Διαθέσιμο Online:https://doi.org/10.1186/s12859-024-05759-w

Διαδίκτυο

https://doi.org/10.1186/s12859-024-05759-w

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