A Heuristic Method for Generating Probabilistic Boolean Networks from a Prescribed Transition Probability Matrix
Probabilistic Boolean Networks (PBNs) have received much attention for modeling genetic regulatory networks. In this paper, we propose efficient algorithms for constructing a probabilistic Boolean network when its transition probability matrix is given. This is an important inverse problem in network inference from steady-state data, as most microarray data sets are assumed to be obtained from sampling the steady-state.
Boolean Networks Probabilistic Boolean Networks Inverse Problem Transition Probability Matrix
Wai-Ki Ching Xi Chen Nam-Kiu Tsing Ho-Yin Leung
Advanced Modeling and Applied Computing Laboratory, Department of Mathematics, The University of Hong Kong, Hong Kong.
国际会议
The Second International Symposium(OSB08)(第二届国际优化及系统生物学学术会议)
云南丽江
英文
271-278
2008-10-31（万方平台首次上网日期，不代表论文的发表时间）