ArticleBioMed research international2014
Sparse representation for tumor classification based on feature extraction using latent low-rank representation.
Article in BioMed research international, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Efficient Techniques Based on Sparse Representation for Classifying High-dimensional Multiclass Microarray Data.Journal of medical signals and sensors · 2026Article
- Tumor classification and biomarker discovery based on the 5'isomiR expression level.BMC cancer · 2019Article
- Prediction of antimicrobial peptides based on sequence alignment and support vector machine-pairwise algorithm utilizing LZ-complexity.BioMed research international · 2015Article
- Sample Selection for Training Cascade Detectors.PloS one · 2015Article
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Authors and funding
4 authors.
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Abstract
Accurate tumor classification is crucial to the proper treatment of cancer. To now, sparse representation (SR) has shown its great performance for tumor classification. This paper conceives a new SR-based method for tumor classification by using gene expression data. In the proposed method, we firstly use latent low-rank representation for extracting salient features and removing noise from the original samples data. Then we use sparse representation classifier (SRC) to build tumor classification model. The experimental results on several real-world data sets show that our method is more efficient and more effective than the previous classification methods including SVM, SRC, and LASSO.
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