Performance evaluation of subspace-based algorithm in selecting differentially expressed genes and classification of tissue types from microarray data
Abstract
This paper presents the implementation and evaluation of subspace-based clustering algorithm for robust selection of differentially expressed genes as well as the classification of tissue types from microarray data. The performance of the proposed algorithm is compared against other well known clustering algorithms and the quality of clusters is evaluated using a number of cluster validation indices. Empirical analyses on a number of synthetic and real microarray data sets suggest that the proposed subspace-based algorithm is robust in selecting differentially expressed genes and performs significantly better compared to popular clustering algorithms in selecting differentially expressed genes and classifying different tissue types. © 2006 IEEE.
Publication Title
IEEE International Conference on Neural Networks - Conference Proceedings
Recommended Citation
Shaik, J., & Yeasin, M. (2006). Performance evaluation of subspace-based algorithm in selecting differentially expressed genes and classification of tissue types from microarray data. IEEE International Conference on Neural Networks - Conference Proceedings, 2972-2979. https://doi.org/10.1109/ijcnn.2006.247253
