scholarly journals Yeast one-hybrid assays for gene-centered human gene regulatory network mapping

2011 ◽  
Vol 8 (12) ◽  
pp. 1050-1052 ◽  
Author(s):  
John S Reece-Hoyes ◽  
A Rasim Barutcu ◽  
Rachel Patton McCord ◽  
Jun Seop Jeong ◽  
Lizhi Jiang ◽  
...  
Keyword(s):  
Gene Regulatory Network ◽  
Regulatory Network ◽  
Human Gene ◽  
Gene Regulatory ◽  
Network Mapping

Integrating genome-scale metabolic modelling and transfer learning for human gene regulatory network reconstruction

2021 ◽  
Author(s):  
Gianvito Pio ◽  
Paolo Mignone ◽  
Giuseppe Magazzù ◽  
Guido Zampieri ◽  
Michelangelo Ceci ◽  
...  
Keyword(s):  
Gene Expression ◽  
Gene Regulatory Network ◽  
Transfer Learning ◽  
Regulatory Network ◽  
Drug Targets ◽  
Human Gene ◽  
Supplementary Information ◽  
Model Organisms ◽  
Metabolic Modelling ◽  
Gene Regulatory

Abstract Motivation Gene regulation is responsible for controlling numerous physiological functions and dynamically responding to environmental fluctuations. Reconstructing the human network of gene regulatory interactions is thus paramount to understanding the cell functional organisation across cell types, as well as to elucidating pathogenic processes and identifying molecular drug targets. Although significant effort has been devoted towards this direction, existing computational methods mainly rely on gene expression levels, possibly ignoring the information conveyed by mechanistic biochemical knowledge. Moreover, except for a few recent attempts, most of the existing approaches only consider the information of the organism under analysis, without exploiting the information of related model organisms. Results We propose a novel method for the reconstruction of the human gene regulatory network, based on a transfer learning strategy that synergically exploits information from human and mouse, conveyed by gene-related metabolic features generated in-silico from gene expression data. Specifically, we learn a predictive model from metabolic activity inferred via tissue-specific metabolic modelling of artificial gene knockouts. Our experiments show that the combination of our transfer learning approach with the constructed metabolic features provides a significant advantage in terms of reconstruction accuracy, as well as additional clues on the contribution of each constructed metabolic feature. Availability The system, the datasets and all the results obtained in this study are available at: https://doi.org/10.6084/m9.figshare.c.5237687 Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Identification of cancer-related genes and motifs in the human gene regulatory network

2015 ◽  
Vol 9 (4) ◽  
pp. 128-134 ◽  
Author(s):  
Matthew B. Carson ◽  
Jianlei Gu ◽  
Guangjun Yu ◽  
Hui Lu
Keyword(s):  
Gene Regulatory Network ◽  
Regulatory Network ◽  
Human Gene ◽  
Gene Regulatory

Regulatory feedback loops bridge the human gene regulatory network and regulate carcinogenesis

2017 ◽  
Vol 20 (3) ◽  
pp. 976-984 ◽  
Author(s):  
Yun-Ru Chen ◽  
Hsuan-Cheng Huang ◽  
Chen-Ching Lin
Keyword(s):  
Gene Regulatory Network ◽  
Regulatory Network ◽  
Human Gene ◽  
Feedback Loops ◽  
Gene Regulatory

scholarly journals The co-regulation mechanism of transcription factors in the human gene regulatory network

2012 ◽  
Vol 40 (18) ◽  
pp. 8849-8861 ◽  
Author(s):  
Junil Kim ◽  
Minsoo Choi ◽  
Jeong-Rae Kim ◽  
Hua Jin ◽  
V. Narry Kim ◽  
...  
Keyword(s):  
Transcription Factors ◽  
Gene Regulatory Network ◽  
Regulatory Network ◽  
Human Gene ◽  
Regulation Mechanism ◽  
Gene Regulatory
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