2 resultados para Annealing model
em Biblioteca Digital da Produção Intelectual da Universidade de São Paulo
Resumo:
Background: In the analysis of effects by cell treatment such as drug dosing, identifying changes on gene network structures between normal and treated cells is a key task. A possible way for identifying the changes is to compare structures of networks estimated from data on normal and treated cells separately. However, this approach usually fails to estimate accurate gene networks due to the limited length of time series data and measurement noise. Thus, approaches that identify changes on regulations by using time series data on both conditions in an efficient manner are demanded. Methods: We propose a new statistical approach that is based on the state space representation of the vector autoregressive model and estimates gene networks on two different conditions in order to identify changes on regulations between the conditions. In the mathematical model of our approach, hidden binary variables are newly introduced to indicate the presence of regulations on each condition. The use of the hidden binary variables enables an efficient data usage; data on both conditions are used for commonly existing regulations, while for condition specific regulations corresponding data are only applied. Also, the similarity of networks on two conditions is automatically considered from the design of the potential function for the hidden binary variables. For the estimation of the hidden binary variables, we derive a new variational annealing method that searches the configuration of the binary variables maximizing the marginal likelihood. Results: For the performance evaluation, we use time series data from two topologically similar synthetic networks, and confirm that our proposed approach estimates commonly existing regulations as well as changes on regulations with higher coverage and precision than other existing approaches in almost all the experimental settings. For a real data application, our proposed approach is applied to time series data from normal Human lung cells and Human lung cells treated by stimulating EGF-receptors and dosing an anticancer drug termed Gefitinib. In the treated lung cells, a cancer cell condition is simulated by the stimulation of EGF-receptors, but the effect would be counteracted due to the selective inhibition of EGF-receptors by Gefitinib. However, gene expression profiles are actually different between the conditions, and the genes related to the identified changes are considered as possible off-targets of Gefitinib. Conclusions: From the synthetically generated time series data, our proposed approach can identify changes on regulations more accurately than existing methods. By applying the proposed approach to the time series data on normal and treated Human lung cells, candidates of off-target genes of Gefitinib are found. According to the published clinical information, one of the genes can be related to a factor of interstitial pneumonia, which is known as a side effect of Gefitinib.
Resumo:
The optoelectronic properties of InAs/GaAs quantum dots can be tuned by rapid thermal annealing. In this study, the morphology change of InAs/GaAs quantum dots layers induced by rapid thermal annealing was investigated at the atomic-scale by cross-sectional scanning tunneling microscopy. Finite elements calculations that model the outward relaxation of the cleaved surface were used to determine the indium composition profile of the wetting layer and the quantum dots prior and post rapid thermal annealing. The results show that the wetting layer is broadened upon annealing. This broadening could be modeled by assuming a random walk of indium atoms. Furthermore, we show that the stronger strain gradient at the location of the quantum dots enhances the intermixing. Photoluminescence measurements show a blueshift and narrowing of the photoluminescence peak. Temperature dependent photoluminescence measurements show a lower activation energy for the annealed sample. These results are in agreement with the observed change in morphology. (C) 2012 American Institute of Physics. [http://dx.doi.org/10.1063/1.4770371]