TY - GEN
T1 - Dynamic gene regulatory network analysis using Saccharomyces cerevisiae large-scale time-course microarray data
AU - Zhang, L.
AU - Wu, H. C.
AU - Lin, J. Q.
AU - Chan, S. C.
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/9/25
Y1 - 2017/9/25
N2 - This paper presents preliminary results and findings of a dynamic gene regulatory network analysis obtained from Saccharomyces cerevisiae (budding yeast) time-course DNA microarray data using a new Alternative Direction Methods of Multipliers (ADMM) based maximum a posteriori probability and time-varying autoregression model (MAP-TVAR) approach. It employs the Li-regularization based sparsity and continuity constraints, which facilitate the identification of sparse GRNs and reduce the estimation variance respectively. Simulation results using synthetic dataset show that the proposed ADMM-based extension not only performs better than our previous work in terms of identification accuracy but also is able to achieve considerable speedup. This enables us to process the whole genome of the budding yeast containing 10,715 genes and 15 timepoints more efficiently. We are able to identify gene interactions aligning well with some natural phenomena and reported in yeast cell cycle related literature. These suggest that the MAP-TVAR approach may serve as a useful tool for large-scale time-varying GRNs analysis using gene microarray data and other related datasets.
AB - This paper presents preliminary results and findings of a dynamic gene regulatory network analysis obtained from Saccharomyces cerevisiae (budding yeast) time-course DNA microarray data using a new Alternative Direction Methods of Multipliers (ADMM) based maximum a posteriori probability and time-varying autoregression model (MAP-TVAR) approach. It employs the Li-regularization based sparsity and continuity constraints, which facilitate the identification of sparse GRNs and reduce the estimation variance respectively. Simulation results using synthetic dataset show that the proposed ADMM-based extension not only performs better than our previous work in terms of identification accuracy but also is able to achieve considerable speedup. This enables us to process the whole genome of the budding yeast containing 10,715 genes and 15 timepoints more efficiently. We are able to identify gene interactions aligning well with some natural phenomena and reported in yeast cell cycle related literature. These suggest that the MAP-TVAR approach may serve as a useful tool for large-scale time-varying GRNs analysis using gene microarray data and other related datasets.
UR - https://www.scopus.com/pages/publications/85032659199
U2 - 10.1109/ISCAS.2017.8050662
DO - 10.1109/ISCAS.2017.8050662
M3 - Conference contribution
AN - SCOPUS:85032659199
T3 - Proceedings - IEEE International Symposium on Circuits and Systems
BT - IEEE International Symposium on Circuits and Systems
T2 - 50th IEEE International Symposium on Circuits and Systems, ISCAS 2017
Y2 - 28 May 2017 through 31 May 2017
ER -