A strategy for meta-analysis of short time series microarray datasets

10.2741/3512 ◽  
2009 ◽  
Vol Volume (14) ◽  
pp. 4058 ◽  
Author(s):  
Ruping Sun
Keyword(s):  
Time Series ◽  
Meta Analysis ◽  
Short Time Series ◽  
Microarray Datasets ◽  
Short Time ◽  
Time Series Microarray

Clustering Short Time-Series Microarray

2008 ◽  
Author(s):  
Loh Wei Ping ◽  
Yahya Abu Hasan ◽  
Kamel Ariffin Mohd Atan ◽  
Isthrinayagy S. Krishnarajah
Keyword(s):  
Time Series ◽  
Short Time Series ◽  
Short Time ◽  
Time Series Microarray

scholarly journals Short time-series microarray analysis: Methods and challenges

2008 ◽  
Vol 2 (1) ◽  
pp. 58 ◽  
Author(s):  
Xuewei Wang ◽  
Ming Wu ◽  
Zheng Li ◽  
Christina Chan
Keyword(s):  
Time Series ◽  
Microarray Analysis ◽  
Short Time Series ◽  
Analysis Methods ◽  
Short Time ◽  
Time Series Microarray

scholarly journals Identifying bidirectional total and non-linear information flow in functional corticomuscular coupling during a dorsiflexion task: a pilot study

2021 ◽  
Vol 18 (1) ◽  
Author(s):  
Tie Liang ◽  
Qingyu Zhang ◽  
Xiaoguang Liu ◽  
Bin Dong ◽  
Xiuling Liu ◽  
...  
Keyword(s):  
Time Series ◽  
Information Flow ◽  
Gamma Band ◽  
Motor Dysfunction ◽  
Beta Band ◽  
Short Time Series ◽  
Information Interaction ◽  
Stroke Group ◽  
Short Time ◽  
Maximal Information Coefficient

Abstract Background The key challenge to constructing functional corticomuscular coupling (FCMC) is to accurately identify the direction and strength of the information flow between scalp electroencephalography (EEG) and surface electromyography (SEMG). Traditional TE and TDMI methods have difficulty in identifying the information interaction for short time series as they tend to rely on long and stable data, so we propose a time-delayed maximal information coefficient (TDMIC) method. With this method, we aim to investigate the directional specificity of bidirectional total and nonlinear information flow on FCMC, and to explore the neural mechanisms underlying motor dysfunction in stroke patients. Methods We introduced a time-delayed parameter in the maximal information coefficient to capture the direction of information interaction between two time series. We employed the linear and non-linear system model based on short data to verify the validity of our algorithm. We then used the TDMIC method to study the characteristics of total and nonlinear information flow in FCMC during a dorsiflexion task for healthy controls and stroke patients. Results The simulation results showed that the TDMIC method can better detect the direction of information interaction compared with TE and TDMI methods. For healthy controls, the beta band (14–30 Hz) had higher information flow in FCMC than the gamma band (31–45 Hz). Furthermore, the beta-band total and nonlinear information flow in the descending direction (EEG to EMG) was significantly higher than that in the ascending direction (EMG to EEG), whereas in the gamma band the ascending direction had significantly higher information flow than the descending direction. Additionally, we found that the strong bidirectional information flow mainly acted on Cz, C3, CP3, P3 and CPz. Compared to controls, both the beta-and gamma-band bidirectional total and nonlinear information flows of the stroke group were significantly weaker. There is no significant difference in the direction of beta- and gamma-band information flow in stroke group. Conclusions The proposed method could effectively identify the information interaction between short time series. According to our experiment, the beta band mainly passes downward motor control information while the gamma band features upward sensory feedback information delivery. Our observation demonstrate that the center and contralateral sensorimotor cortex play a major role in lower limb motor control. The study further demonstrates that brain damage caused by stroke disrupts the bidirectional information interaction between cortex and effector muscles in the sensorimotor system, leading to motor dysfunction.

scholarly journals A reliable measure of similarity based on dependency for short time series: an application to gene expression networks

2009 ◽  
Vol 10 (1) ◽  
pp. 270 ◽  
Author(s):  
Mônica G Campiteli ◽  
Frederico M Soriani ◽  
Iran Malavazi ◽  
Osame Kinouchi ◽  
Carlos AB Pereira ◽  
...  
Keyword(s):  
Gene Expression ◽  
Time Series ◽  
Reliable Measure ◽  
Short Time Series ◽  
Measure Of Similarity ◽  
Short Time

scholarly journals LIMITATIONS OF THE PANEL REGRESSION MODEL APPLICATION: THE EXAMPLE OF THE WESTERN BALKAN COUNTRIES

2021 ◽  
Vol 18 (32) ◽  
Author(s):  
Stanko Stanić ◽  
Bojan Baškot
Keyword(s):  
Time Series ◽  
Regression Model ◽  
Time Frame ◽  
Panel Regression ◽  
Time Dimension ◽  
Data Set ◽  
Short Time Series ◽  
Western Balkan Region ◽  
Balkan Region ◽  
Short Time

Panel regression model may seem like an appealing solution in conditions of limited time series. This is often used as a shortcut to achieve deeper data set by setting several individual cases on the same time dimension, where cross units visually but not really multiply a time frame. Macroeconometrics of the Western Balkan region assumes short time series issue. Additionally, the structural brakes are numerous. Panel regression may seem like a solution, but there are some limitations that should be considered.

Blending neighbor-based and climate-based information to obtain robust low-flow estimates from short time series

2013 ◽  
Vol 49 (12) ◽  
pp. 8017-8025 ◽  
Author(s):  
Pierre Nicolle ◽  
Vazken Andréassian ◽  
Eric Sauquet
Keyword(s):  
Time Series ◽  
Low Flow ◽  
Short Time Series ◽  
Short Time
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