scholarly journals Shortest path analysis using partial correlations for classifying gene functions from gene expression data

2008 ◽  
Vol 25 (1) ◽  
pp. 42-47 ◽  
Author(s):  
A. M. Fitch ◽  
M. B. Jones
Keyword(s):  
Gene Expression ◽  
Path Analysis ◽  
Gene Expression Data ◽  
Shortest Path ◽  
Expression Data ◽  
Gene Functions ◽  
Partial Correlations ◽  
Shortest Path Analysis

scholarly journals dynDeepDRIM: a dynamic deep learning model to infer direct regulatory interactions using single cell time-course gene expression data

2021 ◽  
Author(s):  
Yu Xu ◽  
Jiaxing Chen ◽  
Aiping Lyu ◽  
William K Cheung ◽  
Lu Zhang
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Gene Expression Data ◽  
Time Course ◽  
Target Genes ◽  
Neighborhood Context ◽  
Specific Gene ◽  
Expression Data ◽  
Regulatory Interactions ◽  
Gene Functions

Time-course single-cell RNA sequencing (scRNA-seq) data have been widely applied to reconstruct the cell-type-specific gene regulatory networks by exploring the dynamic changes of gene expression between transcription factors (TFs) and their target genes. The existing algorithms were commonly designed to analyze bulk gene expression data and could not deal with the dropouts and cell heterogeneity in scRNA-seq data. In this paper, we developed dynDeepDRIM that represents gene pair joint expression as images and considers the neighborhood context to eliminate the transitive interactions. dynDeepDRIM integrated the primary image, neighbor images with time-course into a four-dimensional tensor and trained a convolutional neural network to predict the direct regulatory interactions between TFs and genes. We evaluated the performance of dynDeepDRIM on five time-course gene expression datasets. dynDeepDRIM outperformed the state-of-the-art methods for predicting TF-gene direct interactions and gene functions. We also observed gene functions could be better performed if more neighbor images were involved.

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