Functionally classifying genes from microarray data using linear and non-linear data projection
This paper compares the performance of linear and non-linear projection techniques in functionally classifying genes. The performance of both linear and non-linear data projection techniques, namely, Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), Locally Linear Embedding (LLE) and Supervised Locally Linear Embedding (SLLE) were evaluated to project the microarray data onto lower dimensional subspace to estimate the intrinsic dimensionality of the microarray gene expression. Artificially generated (simulated datasets) and real microarray dataset (for example, Sporulation of budding yeast and cancer dataset) have been used to validate the performance of proposed techniques. From the empirical analysis, it was found that the non-linear projection techniques perform better in classifying the gene functions and provide a better visualization of expression profile than the linear methods. © 2006 IEEE.
IEEE International Conference on Computer Systems and Applications, 2006
Shaik, J., & Yeasin, M. (2006). Functionally classifying genes from microarray data using linear and non-linear data projection. IEEE International Conference on Computer Systems and Applications, 2006, 608-615. https://doi.org/10.1109/aiccsa.2006.205152