GEOlimma: differential expression analysis and feature selection using pre-existing microarray data
Abstract Background Differential expression and feature selection analyses are essential steps for the development of accurate diagnostic/prognostic classifiers of complicated human diseases using transcriptomics data. These steps are particularly challenging due to the curse of dimensionality and t...
Main Authors: | Liangqun Lu, Kevin A. Townsend, Bernie J. Daigle |
---|---|
Format: | Article |
Language: | English |
Published: |
BMC
2021-02-01
|
Series: | BMC Bioinformatics |
Subjects: | |
Online Access: | https://doi.org/10.1186/s12859-020-03932-5 |
Similar Items
-
Comprior: facilitating the implementation and automated benchmarking of prior knowledge-based feature selection approaches on gene expression data sets
by: Cindy Perscheid
Published: (2021-08-01) -
ECFS-DEA: an ensemble classifier-based feature selection for differential expression analysis on expression profiles
by: Xudong Zhao, et al.
Published: (2020-02-01) -
An enhanced feature selection filter for classification of microarray cancer data
by: Dilwar Hussain Mazumder, et al.
Published: (2019-05-01) -
A Machine Learning Pipeline for Cancer Detection on Microarray Data: The Role of Feature Discretization and Feature Selection
by: Adara Nogueira, et al.
Published: (2023-08-01) -
Estimating statistical power, posterior probability and publication bias of psychological research using the observed replication rate
by: Michael Ingre, et al.
Published: (2018-01-01)