scholarly journals Variability in cellular gene expression profiles and homeostatic regulation

2015 ◽  
Author(s):  
Yuriy Mishchenko
Keyword(s):  
Gene Expression ◽  
Protein Expression ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Homeostatic Regulation ◽  
Cellular Gene ◽  
Large Variability ◽  
Gene And Protein Expression ◽  
Regulation Mechanisms ◽  
Protein Expression Profiles

One of surprising recent discoveries in biology is that the gene and protein expression profiles in cells with identical genetic and environmental makeup can exhibit large variability. The nature and the significance of this variability had been posed as one of the current fundamental questions in biology. In this letter, we argue that the observed variability in cellular gene and protein expression can be understood as an outcome of homeostatic regulation mechanisms controlling the gene and protein expression profiles.

Gene and protein expression profiles of JAK-STAT signalling pathway in the developing brain of the Ts1Cje down syndrome mouse model

2019 ◽  
Vol 129 (9) ◽  
pp. 871-881 ◽  
Author(s):  
Han-Chung Lee ◽  
Hadri Hadi Md Yusof ◽  
Melody Pui-Yee Leong ◽  
Shahidee Zainal Abidin ◽  
Eryse Amira Seth ◽  
...  
Keyword(s):  
Down Syndrome ◽  
Mouse Model ◽  
Protein Expression ◽  
Expression Profiles ◽  
Developing Brain ◽  
Signalling Pathway ◽  
Gene And Protein Expression ◽  
Protein Expression Profiles

Gene and Protein Expression Profiles ofShewanella oneidensisduring Anaerobic Growth with Different Electron Acceptors

2002 ◽  
Vol 6 (1) ◽  
pp. 39-60 ◽  
Author(s):  
Alex S. Beliaev ◽  
Dorothea K. Thompson ◽  
Tripti Khare ◽  
Hanjo Lim ◽  
Craig C. Brandt ◽  
...  
Keyword(s):  
Protein Expression ◽  
Expression Profiles ◽  
Electron Acceptors ◽  
Anaerobic Growth ◽  
Gene And Protein Expression ◽  
Protein Expression Profiles

scholarly journals Cellular Functions and Gene and Protein Expression Profiles in Endothelial Cells Derived from Moyamoya Disease-Specific iPS Cells

PLoS ONE ◽  
2016 ◽  
Vol 11 (9) ◽  
pp. e0163561 ◽  
Author(s):  
Shuji Hamauchi ◽  
Hideo Shichinohe ◽  
Haruto Uchino ◽  
Shigeru Yamaguchi ◽  
Naoki Nakayama ◽  
...  
Keyword(s):  
Endothelial Cells ◽  
Protein Expression ◽  
Moyamoya Disease ◽  
Expression Profiles ◽  
Ips Cells ◽  
Cellular Functions ◽  
Gene And Protein Expression ◽  
Protein Expression Profiles ◽  
Disease Specific

A deep learning model based on sparse auto-encoder for prioritizing cancer-related genes and drug target combinations

2019 ◽  
Vol 40 (5) ◽  
pp. 624-632
Author(s):  
Ji-Wei Chang ◽  
Yuduan Ding ◽  
Muhammad Tahir ul Qamar ◽  
Yin Shen ◽  
Junxiang Gao ◽  
...  
Keyword(s):  
Gene Expression ◽  
Deep Learning ◽  
Protein Expression ◽  
Protein Interaction ◽  
Targeted Therapies ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Expression Data ◽  
Related Proteins ◽  
Deep Learning Model

Abstract Prioritization of cancer-related genes from gene expression profiles and proteomic data is vital to improve the targeted therapies research. Although computational approaches have been complementing high-throughput biological experiments on the understanding of human diseases, it still remains a big challenge to accurately discover cancer-related proteins/genes via automatic learning from large-scale protein/gene expression data and protein–protein interaction data. Most of the existing methods are based on network construction combined with gene expression profiles, which ignore the diversity between normal samples and disease cell lines. In this study, we introduced a deep learning model based on a sparse auto-encoder to learn the specific characteristics of protein interactions in cancer cell lines integrated with protein expression data. The model showed learning ability to identify cancer-related proteins/genes from the input of different protein expression profiles by extracting the characteristics of protein interaction information, which could also predict cancer-related protein combinations. Comparing with other reported methods including differential expression and network-based methods, our model got the highest area under the curve value (>0.8) in predicting cancer-related genes. Our study prioritized ~500 high-confidence cancer-related genes; among these genes, 211 already known cancer drug targets were found, which supported the accuracy of our method. The above results indicated that the proposed auto-encoder model could computationally prioritize candidate proteins/genes involved in cancer and improve the targeted therapies research.

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