ArticleBMC bioinformatics2021
ILRC: a hybrid biomarker discovery algorithm based on improved L1 regularization and clustering in microarray data.
Article in BMC bioinformatics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- BAMBI integrates biostatistical and artificial intelligence methods to improve RNA biomarker discovery.Briefings in bioinformatics · 2025Article
- A two-stage hybrid biomarker selection method based on ensemble filter and binary differential evolution incorporating binary African vultures optimization.BMC bioinformatics · 2023Article
- The Prognostic Value and Immune Landscapes of a mFrontiers in cell and developmental biology · 2021Article
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Authors and funding
4 authors.
Funding
Abstract
backgroundFinding significant genes or proteins from gene chip data for disease diagnosis and drug development is an important task. However, the challenge comes from the curse of the data dimension. It is of great significance to use machine learning methods to find important features from the data and build an accurate classification model.
resultsThe proposed method has proved superior to the published advanced hybrid feature selection method and traditional feature selection method on different public microarray data sets. In addition, the biomarkers selected using our method show a match to those provided by the cooperative hospital in a set of clinical cleft lip and palate data.
methodIn this paper, a feature selection algorithm ILRC based on clustering and improved L1 regularization is proposed. The features are firstly clustered, and the redundant features in the sub-clusters are deleted. Then all the remaining features are iteratively evaluated using ILR. The final result is given according to the cumulative weight reordering.
conclusionThe proposed method can effectively remove redundant features. The algorithm's output has high stability and classification accuracy, which can potentially select potential biomarkers.
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