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 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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