Modelling Nonstationary Gene Regulatory Processes
نویسندگان
چکیده
منابع مشابه
Modelling Nonstationary Gene Regulatory Processes
An important objective in systems biology is to infer gene regulatory networks from postgenomic data, and dynamic Bayesian networks have been widely applied as a popular tool to this end. The standard approach for nondiscretised data is restricted to a linear model and a homogeneous Markov chain. Recently, various generalisations based on changepoint processes and free allocation mixture models...
متن کاملModelling of Nonstationary Processes Using Radial Basis Function Networks'
This paper reports preliminary progress on a principled approach to modelling nonstationary phenomena using neural networks. We are concerned with both parameter and model order complexity estimation. The basic methodology assumes a Bayesian foundation. However t o allow the construction of pragmatic models, successive approximations have to be made l o permit computational tractibility. The lo...
متن کاملNonstationary Continuous - Time Processes ∗
∗Preliminary Comments are welcome. Paper written for the Handbook of Financial Econometrics edited by Yacine Aı̈t-Sahalia and Lars Peter Hansen. We thank Darrell Duffie, Benoit Perron and Mark Watson for discussions and Seoyeon Lee for research assistance. Bandi acknowledges financial support from the IBM Corporation Faculty Research Fund at the University of Chicago. Phillips thanks fhe NSF for...
متن کاملModelling Nonstationary Dynamics
We incorporate the use of validation data to cope with noisy records in a neural network-based method for modelling the dynamics of slowly-changing nonstationary systems. As a byproduct, we obtain a precise criterion to find the optimal value of a required internal hyperparameter. Testing these ideas on a controlled problem shows that the resulting algorithm is able to outperform previous metho...
متن کاملModelling slowly changing dynamic gene-regulatory networks
Dynamic gene-regulatory networks are complex since the number of potential components involved in the system is very large. Estimating dynamic networks is an important task because they compromise valuable information about interactions among genes. Graphical models are a powerful class of models to estimate conditional independence among random variables, e.g. interactions in dynamic systems. ...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Advances in Bioinformatics
سال: 2010
ISSN: 1687-8027,1687-8035
DOI: 10.1155/2010/749848