TY - JOUR AU - Lê Cao, Kim-Anh AU - Boitard, Simon AU - Besse, Philippe PY - 2011 DA - 2011/06/22 TI - Sparse PLS discriminant analysis: biologically relevant feature selection and graphical displays for multiclass problems JO - BMC Bioinformatics SP - 253 VL - 12 IS - 1 AB - Variable selection on high throughput biological data, such as gene expression or single nucleotide polymorphisms (SNPs), becomes inevitable to select relevant information and, therefore, to better characterize diseases or assess genetic structure. There are different ways to perform variable selection in large data sets. Statistical tests are commonly used to identify differentially expressed features for explanatory purposes, whereas Machine Learning wrapper approaches can be used for predictive purposes. In the case of multiple highly correlated variables, another option is to use multivariate exploratory approaches to give more insight into cell biology, biological pathways or complex traits. SN - 1471-2105 UR - https://doi.org/10.1186/1471-2105-12-253 DO - 10.1186/1471-2105-12-253 ID - Lê Cao2011 ER -