Inference of Gene Regulatory Network from Time Series Expression Data ...
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Aug 9, 2021 · In this paper, we introduce local geometric similarity and multivariate regression (LESME) to infer gene regulatory networks from time-course gene expression ...
In this paper, we introduce local geometric similarity and multivariate regression (LESME) to infer gene regulatory networks from time-course gene expression ...
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What is gene regulatory network inference?
Why does changing regulatory sequences have a large impact on gene expression?
Inference of Gene Regulatory Network from Time Series Expression Data by Combining Local Geometric Similarity and Multivariate Regression. Guangyi Chen ...
We have reviewed the modeling and inference of Gene Regulatory Network (GRN) from time-series data, categorized into those focused on structure, or those on ...
Missing: Combining Geometric
We proposed a novel method, GNIPLR (Gene networks inference based on projection and lagged regression) to infer GRNs from time-series or non-time-series gene ...
May 26, 2021 · High-throughput transcriptomic datasets are often examined to discover new actors and regulators of a biological response.
Missing: Geometric | Show results with:Geometric
In this article, we proposed an interpretable machine-learning framework to infer the gene regulatory network from time-series expression data. We applied ...
[PDF] Inferring Gene Regulatory Networks from Time Series Microarray Data
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Inference of gene regulatory network from time series gene expression data is a very challenging task for computational biologists. Lots of mathematical ...
This review focuses on the representative and popular GRN inference approaches which utilize single-cell sequencing data especially on those with multi-omics ...
This work proposes a novel method based on a pool of neural networks for obtaining a gene regulatory network from a gene expression dataset, ...