Multi-resolution independent component analysis for high-performance tumor classification and biomarker discovery

• Main Authors: Han, H, Li, X.-L
• Other Authors: DEPARTMENT OF COMPUTER SCIENCE
• Format: Article
• Published: 2020
• Subjects: Classification algorithm; Experimental comparison; Feature selection methods; Independent component analysis(ICA); Linear discriminant analysis; Local feature extraction; Nonnegative matrix factorization; State-of-the-art algorithms; Algorithms; Classifiers; Diagnosis; Diseases; Feature extraction; Gene expression; Independent component analysis; Mica; Principal component analysis; Support vector machines; Tumors; Bioinformatics; Data mining; tumor marker; algorithm; article; classification; comparative study; computer program; discriminant analysis; DNA microarray; gene expression profiling; genetics; methodology; neoplasm; principal component analysis; sensitivity and specificity; Discriminant Analysis; Gene Expression Profiling; Neoplasms; Oligonucleotide Array Sequence Analysis; Principal Component Analysis; Sensitivity and Specificity; Software; Tumor Markers, Biological
• Online Access: https://scholarbank.nus.edu.sg/handle/10635/181643

10.1186/1471-2105-12-S1-S7