ArticleNeuroImage2024
Local-structure-preservation and redundancy-removal-based feature selection method and its application to the identification of biomarkers for schizophrenia.
Article in NeuroImage, 2024. 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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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
3 citing papers in PubMed.
- An Orthogonal Semi-Nonnegative Matrix Factorization Method for Dynamic Functional Connectivity Analysis and Its Application to Schizophrenia.IEEE journal of biomedical and health informatics · 2026Article
- A prior-knowledge-guided feature selection method and its application to biomarker identification of schizophrenia.Network neuroscience (Cambridge, Mass.) · 2025Article
- Biomarkers in schizophrenia - past, present and future.Romanian journal of morphology and embryology = Revue roumaine de morphologie et embryologieReview
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Authors and funding
5 authors.
Funding
Abstract
Accurate diagnosis of mental disorders is expected to be achieved through the identification of reliable neuroimaging biomarkers with the help of cutting-edge feature selection techniques. However, existing feature selection methods often fall short in capturing the local structural characteristics among samples and effectively eliminating redundant features, resulting in inadequate performance in disorder prediction. To address this gap, we propose a novel supervised method named local-structure-preservation and redundancy-removal-based feature selection (LRFS), and then apply it to the identification of meaningful biomarkers for schizophrenia (SZ). LRFS method leverages graph-based regularization to preserve original sample similarity relationships during data transformation, thus retaining crucial local structure information. Additionally, it introduces redundancy-removal regularization based on interrelationships among features to exclude similar and redundant features from high-dimensional data. Moreover, LRFS method incorporates l
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