TY - JOUR
T1 - ICA and PLS modeling for functional analysis and drug sensitivity for DNA microarray signals
AU - Liao, Xuejun
AU - Dasgupta, Nilanjan
AU - Lin, Simon M.
AU - Carin, Lawrence
N1 - Generated from Scopus record by KAUST IRTS on 2021-02-09
PY - 2002/1/1
Y1 - 2002/1/1
N2 - The DNA microarray technique offers an ability to analyze the expression profile of a genome. The complex correlation between the large number of genes present in the genome undermines straightforward understanding of their functionality. In this paper, we have proposed a pair of modeling schemes to recognize the functional identities of the known genes. In Independent Component Analysis (ICA), each of the microarray signals is modeled as a linear combination of some underlying independent components having specific biological interpretation. The second algorithm, Partial Least Squares (PLS) is proposed to identify the latent functional units contributing to drug sensitivity from the microarray data. Applications of this research include prediction of drug responses based on gene expressions, and also to identify the function(s) of a new gene. We consider the Rosetta compendium data set with yeast gene profiles, and the NCI-60 data set of human gene expressions as a function of drug type (cancer drugs are considered).
AB - The DNA microarray technique offers an ability to analyze the expression profile of a genome. The complex correlation between the large number of genes present in the genome undermines straightforward understanding of their functionality. In this paper, we have proposed a pair of modeling schemes to recognize the functional identities of the known genes. In Independent Component Analysis (ICA), each of the microarray signals is modeled as a linear combination of some underlying independent components having specific biological interpretation. The second algorithm, Partial Least Squares (PLS) is proposed to identify the latent functional units contributing to drug sensitivity from the microarray data. Applications of this research include prediction of drug responses based on gene expressions, and also to identify the function(s) of a new gene. We consider the Rosetta compendium data set with yeast gene profiles, and the NCI-60 data set of human gene expressions as a function of drug type (cancer drugs are considered).
UR - http://ieeexplore.ieee.org/document/5745504/
UR - http://www.scopus.com/inward/record.url?scp=18144445361&partnerID=8YFLogxK
U2 - 10.1109/ICASSP.2002.5745504
DO - 10.1109/ICASSP.2002.5745504
M3 - Article
SN - 1520-6149
VL - 4
SP - 3880
EP - 3883
JO - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
JF - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
ER -
