Approaching Causality: Discovering Time-Lag Correlations in Genetic Expression Data with Static and Dynamic Relevance Networks
نویسندگان
چکیده
Recent advances in micro-array technology have allowed gene expression measurements to be made on a whole-genome scale. Previous research has focused on identifying related genes by studying related simultaneous patterns of gene expression [2, 4]. Other research has focused on studying the simultaneous dynamics, or rate of change, of gene expression [5]. In this study, we focus on identifying genes based on non-simultaneous correlated behavior patterns of expression.
منابع مشابه
Evaluating the importance of dynamic allocation and routing of rescuers in reducing response time
Due to delay in receiving emergency medical services, a high number of injured people and patients annually lose their lives. Determining the medical service area and correct routing of rescuing operation is influential on the reduction of rescuers’ response time. Changing the traffic flow leads to change of medical service area. Therefore, it is expected that by observing changing traffic, the...
متن کاملExperimental assessment of static and dynamic algorithms for gene regulation inference from time series expression data
Accurate inference of causal gene regulatory networks from gene expression data is an open bioinformatics challenge. Gene interactions are dynamical processes and consequently we can expect that the effect of any regulation action occurs after a certain temporal lag. However such lag is unknown a priori and temporal aspects require specific inference algorithms. In this paper we aim to assess t...
متن کاملClassification and Comparison of Methods for Discovering Coverage Loss Areas in Wireless Sensor Networks
In recent years, wireless sensor networks data is taken into consideration as an ideal source, in terms of speed, accuracy and cost, in order to study the Earth's surface. One of the most important challenges in this area, is the signaling network coverage and finding holes. In recent years, wireless sensor networks data is taken into consideration as an ideal source, in terms of speed, accurac...
متن کاملH∞ Sampled-Data Controller Design for Stochastic Genetic Regulatory Networks
Artificially regulating gene expression is an important step in developing new treatment for system-level disease such as cancer. In this paper, we propose a method to regulate gene expression based on sampled-data measurements of gene products concentrations. Inherent noisy behaviour of Gene regulatory networks are modeled with stochastic nonlinear differential equation. To synthesize feed...
متن کاملRelational mining for discovering changes in evolving networks
Networks are data structures more and more frequently used for modeling interactions in social and biological phenomena, as well as between various types of devices, tools and machines. They can be either static or dynamic, dependently on whether the modeled interactions are fixed or changeable over time. Static networks have been extensively investigated in data mining, while fewer studies hav...
متن کامل