scholarly journals Prediction of Bioluminescent Proteins Using Auto Covariance Transformation of Evolutional Profiles

2012 ◽  
Vol 13 (3) ◽  
pp. 3650-3660 ◽  
Author(s):  
Xiaowei Zhao ◽  
Jiakui Li ◽  
Yanxin Huang ◽  
Zhiqiang Ma ◽  
Minghao Yin
Keyword(s):  
Auto Covariance ◽  
Auto Covariance Transformation

Accurate prediction of protein structural class using auto covariance transformation of PSI-BLAST profiles

Amino Acids ◽  
2011 ◽  
Vol 42 (6) ◽  
pp. 2243-2249 ◽  
Author(s):  
Taigang Liu ◽  
Xingbo Geng ◽  
Xiaoqi Zheng ◽  
Rensuo Li ◽  
Jun Wang
Keyword(s):  
Accurate Prediction ◽  
Structural Class ◽  
Protein Structural Class ◽  
Auto Covariance ◽  
Auto Covariance Transformation

scholarly journals Using position specific scoring matrix and auto covariance to predict protein subnuclear localization

2009 ◽  
Vol 02 (01) ◽  
pp. 51-56 ◽  
Author(s):  
Rong-Quan Xiao ◽  
Yan-Zhi Guo ◽  
Yu-Hong Zeng ◽  
Hai-Feng Tan ◽  
Hai-Feng Tan ◽  
...  
Keyword(s):  
Position Specific Scoring Matrix ◽  
Subnuclear Localization ◽  
Scoring Matrix ◽  
Auto Covariance

A novel dynamical community detection algorithm based on weighting scheme

2015 ◽  
Vol 26 (08) ◽  
pp. 1550091 ◽  
Author(s):  
Ju Li ◽  
Kai Yu ◽  
Ke Hu
Keyword(s):  
Detection Algorithm ◽  
Weighting Scheme ◽  
Overlapping Communities ◽  
Structure Detection ◽  
Stop Time ◽  
Markov Random Walk ◽  
Link Strength ◽  
Community Detection Algorithm ◽  
Auto Covariance ◽  
Membership Vector

Network dynamics plays an important role in analyzing the correlation between the function properties and the topological structure. In this paper, we propose a novel dynamical iteration (DI) algorithm, which incorporates the iterative process of membership vector with weighting scheme, i.e. weighting W and tightness T. These new elements can be used to adjust the link strength and the node compactness for improving the speed and accuracy of community structure detection. To estimate the optimal stop time of iteration, we utilize a new stability measure which is defined as the Markov random walk auto-covariance. We do not need to specify the number of communities in advance. It naturally supports the overlapping communities by associating each node with a membership vector describing the node's involvement in each community. Theoretical analysis and experiments show that the algorithm can uncover communities effectively and efficiently.

scholarly journals Analysis of parameters of railway bridge vibration caused by moving rail vehicles

2019 ◽  
Vol 52 (9-10) ◽  
pp. 1210-1219 ◽  
Author(s):  
Arturas Kilikevicius ◽  
Antanas Fursenko ◽  
Mindaugas Jurevicius ◽  
Kristina Kilikeviciene ◽  
Gintautas Bureika
Keyword(s):  
Function Method ◽  
Dynamic Loads ◽  
Railway Bridge ◽  
Vibration Signals ◽  
Railway Bridges ◽  
Covariance Functions ◽  
Rail Vehicles ◽  
Measurement Results ◽  
Static And Dynamic Loads ◽  
Auto Covariance

The durability and endurance of the exploitation of railway bridges depend on the intensity and the manner of the static and dynamic loads affecting them. Heavy freight trains passing the bridges cause huge vertical and dual-axis vibrations, which are evidenced by the fatigue of the construction of the bridge. The scatter of the acceleration intensity of the railway bridge vibratory oscillation and their parameters are analysed while applying the theory of covariation functions. The results of the measurements of the intensity of the acceleration of vibratory oscillation in the points of support beams recorded in the timescale in the form of arrays (matrixes). Applied covariance function method provides measurement equipment and experimental simplicity of the analysis of the railway bridge vibration signals. The standard scores of mutual covariation functions of the array of the measurement results of the acceleration of digital vibratory oscillation and the standard scores of the auto-covariance functions of separate arrays are altering considering the interval of quantization in the timescale. Results gained by analysis prove the advantage of the rationed auto-covariational functions method while analysing the dynamic oscillation of bridges.

scholarly journals acACS: Improving the Prediction Accuracy of Protein Subcellular Locations and Protein Classification by Incorporating the Average Chemical Shifts Composition

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Guo-Liang Fan ◽  
Yan-Ling Liu ◽  
Yong-Chun Zuo ◽  
Han-Xue Mei ◽  
Yi Rang ◽  
...  
Keyword(s):  
Chemical Shift ◽  
Prediction Accuracy ◽  
Structural Changes ◽  
Chemical Shifts ◽  
Protein Classification ◽  
Structure Information ◽  
Local Environments ◽  
Protein Subcellular Locations ◽  
Auto Covariance ◽  
Online Web

The chemical shift is sensitive to changes in the local environments and can report the structural changes. The structure information of a protein can be represented by the average chemical shifts (ACS) composition, which has been broadly applied for enhancing the prediction accuracy in protein subcellular locations and protein classification. However, different kinds of ACS composition can solve different problems. We established an online web server named acACS, which can convert secondary structure into average chemical shift and then compose the vector for representing a protein by using the algorithm of auto covariance. Our solution is easy to use and can meet the needs of users.

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