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Causal regulatory network
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Transcriptome time series can be used to track the expression of genes during development, allowing the timing, intensity, and dynamics of genetic programmes to be determined. Furthermore, time series analysis can reveal causal relationships between genes, leading to an understanding of how the regulatory networks are rewired during development.
10p
vijichea2711
28-05-2021
9
1
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Our study focuses on discovering gene regulatory networks from time series gene expression data using the Granger causality (GC) model. However, the number of available time points (T) usually is much smaller than the number of target genes (n) in biological datasets.
18p
vikentucky2711
24-11-2020
12
2
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Inferring gene regulatory network (GRN) has been an important topic in Bioinformatics. Many computational methods infer the GRN from high-throughput expression data. Due to the presence of time delays in the regulatory relationships, High-Order Dynamic Bayesian Network (HO-DBN) is a good model of GRN.
28p
vioklahoma2711
19-11-2020
13
2
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Inference of active regulatory cascades under specific molecular and environmental perturbations is a recurring task in transcriptional data analysis. Commercial tools based on large, manually curated networks of causal relationships offering such functionality have been used in thousands of articles in the biomedical literature.
15p
vioklahoma2711
19-11-2020
12
1
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Common and complex traits are the consequence of the interaction and regulation of multiple genes simultaneously, therefore characterizing the intercon‑ nectivity of genes is essential to unravel the underlying biological networks.
19p
vicolorado2711
22-10-2020
10
0
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Lung adenocarcinoma (LUAD) is one of the most common cancer types, threatening the human health around the world. However, the high heterogeneity and complexity of LUAD limit the benefits of targeted therapies.
12p
vikuala271
13-06-2020
10
1
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