scholarly journals Corrigendum to “Expression and Gene Regulation Network of Metabolic Enzyme Phosphoglycerate Mutase Enzyme 1 in Breast Cancer Based on Data Mining”

2021 ◽  
Vol 2021 ◽  
pp. 1-2
Author(s):  
Yongxuan Wang ◽  
Xifeng Xiong ◽  
Xing Hua ◽  
Wei Liu
Keyword(s):  
Breast Cancer ◽  
Data Mining ◽  
Gene Regulation ◽  
Phosphoglycerate Mutase ◽  
Metabolic Enzyme ◽  
Gene Regulation Network ◽  
Regulation Network

scholarly journals Expression and gene regulation network of RBM8A in hepatocellular carcinoma based on data mining

Aging ◽  
2019 ◽  
Vol 11 (2) ◽  
pp. 423-447 ◽  
Author(s):  
Yan Lin ◽  
Rong Liang ◽  
Yufen Qiu ◽  
Yufeng Lv ◽  
Jinyan Zhang ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Data Mining ◽  
Gene Regulation ◽  
Gene Regulation Network ◽  
Regulation Network

Expression and Gene Regulation Network of ELF3 in Breast Invasive Carcinoma Based on Data Mining

2020 ◽  
pp. 514-523
Author(s):  
Chenxia Ren ◽  
Pengyong Han ◽  
Chandrasekhar Gopalakrishnan ◽  
Caixia Xu ◽  
Rajasekaran Ramalingam ◽  
...  
Keyword(s):  
Data Mining ◽  
Gene Regulation ◽  
Invasive Carcinoma ◽  
Gene Regulation Network ◽  
Regulation Network

scholarly journals Expression and Gene Regulation Network of Metabolic Enzyme Phosphoglycerate Mutase Enzyme 1 in Breast Cancer Based on Data Mining

2021 ◽  
Vol 2021 ◽  
pp. 1-39
Author(s):  
Yongxuan Wang ◽  
Xifeng Xiong ◽  
Xing Hua ◽  
Wei Liu
Keyword(s):  
Breast Cancer ◽  
Data Mining ◽  
Transcription Factors ◽  
Cancer Progression ◽  
Molecular Subtype ◽  
Expression Level ◽  
Phosphoglycerate Mutase ◽  
Metabolic Enzyme ◽  
Sequencing Data ◽  
Her2 Positive

The metabolic enzyme phosphoglycerate mutase enzyme 1 (PGAM1) is a key enzyme in the glycolysis pathway, and glycolysis is closely related to cancer progression, suggesting that PGAM1 may have important functions in breast cancer. We used sequencing data from the Oncomine database and UALCAN database to analyze the expression of PGAM1 and its influence on the clinicopathological characteristics of breast cancer. LinkedOmics was used to identify genes related to PGAM1 expression, kinases, miRNAs, and transcription factors that were significantly related to PGAM1 through GSEA. cBioPortal was used to identify the alternation frequency and form of PGAM1 in breast cancer. The expression level of PGAM1 in breast cancer was significantly higher than that in normal tissues. Moreover, the expression level of PGAM1 is closely related to the molecular subtype and TP53 mutation status. The expression level of PGAM1 in HER2-positive and triple-negative tumors was significantly higher than that of luminal type. The expression level of PGAM1 in TP53-mutant tumors was higher than that in non-TP53-mutant tumors. In addition, the overall survival of patients with high PGAM1 expression was significantly worse than that of patients with low expression ( P = 0.0077 ). Through GSEA analysis, we found multiple kinases, miRNAs, and transcription factors significantly related to PFKFB4. cBioPortal analysis showed that the mutation rate of PGAM1 in breast cancer was relatively low (4%), and the main form of mutation was high mRNA expression. This study suggests that PGAM1 is a potential diagnostic and prognostic marker in breast cancer. Through data mining, we revealed the potential regulatory network information of PGAM1, laying a foundation for further research on the role of PGAM1 in breast cancer.

scholarly journals Deciphering gene regulation from gene expression dynamics using deep neural network

2018 ◽  
Author(s):  
Jingxiang Shen ◽  
Mariela D. Petkova ◽  
Yuhai Tu ◽  
Feng Liu ◽  
Chao Tang
Keyword(s):  
Neural Network ◽  
Gene Expression ◽  
Machine Learning ◽  
Gene Regulation ◽  
Gene Network ◽  
Deep Neural Network ◽  
Biological Functions ◽  
Gene Regulation Network ◽  
Regulation Network ◽  
Gene Expression Dynamics

AbstractComplex biological functions are carried out by the interaction of genes and proteins. Uncovering the gene regulation network behind a function is one of the central themes in biology. Typically, it involves extensive experiments of genetics, biochemistry and molecular biology. In this paper, we show that much of the inference task can be accomplished by a deep neural network (DNN), a form of machine learning or artificial intelligence. Specifically, the DNN learns from the dynamics of the gene expression. The learnt DNN behaves like an accurate simulator of the system, on which one can perform in-silico experiments to reveal the underlying gene network. We demonstrate the method with two examples: biochemical adaptation and the gap-gene patterning in fruit fly embryogenesis. In the first example, the DNN can successfully find the two basic network motifs for adaptation – the negative feedback and the incoherent feed-forward. In the second and much more complex example, the DNN can accurately predict behaviors of essentially all the mutants. Furthermore, the regulation network it uncovers is strikingly similar to the one inferred from experiments. In doing so, we develop methods for deciphering the gene regulation network hidden in the DNN “black box”. Our interpretable DNN approach should have broad applications in genotype-phenotype mapping.SignificanceComplex biological functions are carried out by gene regulation networks. The mapping between gene network and function is a central theme in biology. The task usually involves extensive experiments with perturbations to the system (e.g. gene deletion). Here, we demonstrate that machine learning, or deep neural network (DNN), can help reveal the underlying gene regulation for a given function or phenotype with minimal perturbation data. Specifically, after training with wild-type gene expression dynamics data and a few mutant snapshots, the DNN learns to behave like an accurate simulator for the genetic system, which can be used to predict other mutants’ behaviors. Furthermore, our DNN approach is biochemically interpretable, which helps uncover possible gene regulatory mechanisms underlying the observed phenotypic behaviors.

scholarly journals Mining database for the expression and gene regulation network of JAK2 in skin cutaneous melanoma

Life Sciences ◽  
2020 ◽  
Vol 253 ◽  
pp. 117600 ◽  
Author(s):  
Wancong Zhang ◽  
Hanxing Zhao ◽  
Jiasheng Chen ◽  
Xiaoping Zhong ◽  
Weiping Zeng ◽  
...  
Keyword(s):  
Gene Regulation ◽  
Cutaneous Melanoma ◽  
Gene Regulation Network ◽  
Regulation Network
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