Modeling T-cell activation using gene expression profiling and state-space models
نویسندگان
چکیده
منابع مشابه
Modeling T-cell activation using gene expression profiling and state-space models
MOTIVATION We have used state-space models to reverse engineer transcriptional networks from highly replicated gene expression profiling time series data obtained from a well-established model of T-cell activation. State space models are a class of dynamic Bayesian networks that assume that the observed measurements depend on some hidden state variables that evolve according to Markovian dynami...
متن کاملModeling of Gene Regulatory Networks Using State Space Models
Computational Genomics is now becoming the growing area for researchers to decipher biology from genome sequences and related high throughput data. In the post genomic era, there is huge amount of genomic data available because of different advanced experimental technology like microarray technology, Chromatin immune-precipitation with array hybridization (ChIP-chip) etc. [1]. In order to analy...
متن کاملModeling Volatility Using State Space Models
In time series problems, noise can be divided into two categories: dynamic noise which drives the process, and observational noise which is added in the measurement process, but does not influence future values of the system. In this framework, we show that empirical volatilities (the squared relative returns of prices) exhibit a significant amount of observational noise. To model and predict t...
متن کاملModeling Stock Return Volatility Using Symmetric and Asymmetric Nonlinear State Space Models: Case of Tehran Stock Market
Volatility is a measure of uncertainty that plays a central role in financial theory, risk management, and pricing authority. Turbulence is the conditional variance of changes in asset prices that is not directly observable and is considered a hidden variable that is indirectly calculated using some approximations. To do this, two general approaches are presented in the literature of financial ...
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: Bioinformatics
سال: 2004
ISSN: 1367-4803,1460-2059
DOI: 10.1093/bioinformatics/bth093