A Review of Microarray Datasets: Where to Find Them and Specific Characteristics

2019 ◽  
pp. 65-85
Author(s):  
Amparo Alonso-Betanzos ◽  
Verónica Bolón-Canedo ◽  
Laura Morán-Fernández ◽  
Noelia Sánchez-Maroño
Keyword(s):  
Microarray Datasets

scholarly journals Rewired Pathways and Disrupted Pathway Crosstalk in Schizophrenia Transcriptomes by Multiple Differential Coexpression Methods

Genes ◽  
2021 ◽  
Vol 12 (5) ◽  
pp. 665
Author(s):  
Hui Yu ◽  
Yan Guo ◽  
Jingchun Chen ◽  
Xiangning Chen ◽  
Peilin Jia ◽  
...  
Keyword(s):  
Gene Expression ◽  
Focal Adhesion ◽  
Biological Pathways ◽  
Postmortem Brain ◽  
Rna Seq ◽  
Hub Genes ◽  
Differential Coexpression ◽  
Pathway Crosstalk ◽  
Microarray Datasets ◽  
Transcriptomic Studies

Transcriptomic studies of mental disorders using the human brain tissues have been limited, and gene expression signatures in schizophrenia (SCZ) remain elusive. In this study, we applied three differential co-expression methods to analyze five transcriptomic datasets (three RNA-Seq and two microarray datasets) derived from SCZ and matched normal postmortem brain samples. We aimed to uncover biological pathways where internal correlation structure was rewired or inter-coordination was disrupted in SCZ. In total, we identified 60 rewired pathways, many of which were related to neurotransmitter, synapse, immune, and cell adhesion. We found the hub genes, which were on the center of rewired pathways, were highly mutually consistent among the five datasets. The combinatory list of 92 hub genes was generally multi-functional, suggesting their complex and dynamic roles in SCZ pathophysiology. In our constructed pathway crosstalk network, we found “Clostridium neurotoxicity” and “signaling events mediated by focal adhesion kinase” had the highest interactions. We further identified disconnected gene links underlying the disrupted pathway crosstalk. Among them, four gene pairs (PAK1:SYT1, PAK1:RFC5, DCTN1:STX1A, and GRIA1:MAP2K4) were normally correlated in universal contexts. In summary, we systematically identified rewired pathways, disrupted pathway crosstalk circuits, and critical genes and gene links in schizophrenia transcriptomes.

scholarly journals GTI: A Novel Algorithm for Identifying Outlier Gene Expression Profiles from Integrated Microarray Datasets

PLoS ONE ◽  
2011 ◽  
Vol 6 (2) ◽  
pp. e17259 ◽  
Author(s):  
John Patrick Mpindi ◽  
Henri Sara ◽  
Saija Haapa-Paananen ◽  
Sami Kilpinen ◽  
Tommi Pisto ◽  
...  
Keyword(s):  
Gene Expression ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Microarray Datasets ◽  
Novel Algorithm

scholarly journals Gene Selection and Classification in Microarray Datasets using a Hybrid Approach of PCC-BPSO/GA with Multi Classifiers

2018 ◽  
Vol 14 (6) ◽  
pp. 868-880 ◽  
Author(s):  
Shilan S. Hameed ◽  
Fahmi F. Muhammad ◽  
Rohayanti Hassan ◽  
Faisal Saeed
Keyword(s):  
Gene Selection ◽  
Hybrid Approach ◽  
Microarray Datasets

scholarly journals Investigation of genes in chronic and acute morphine-treated mice using microarray datasets

2015 ◽  
Vol 14 (3) ◽  
pp. 10193-10205 ◽  
Author(s):  
L. Ding ◽  
J.L. Zhang ◽  
S.H. Yu ◽  
L.F. Sheng
Keyword(s):  
Microarray Datasets

scholarly journals Gene Selection in Cancer Classification Using Sparse Logistic Regression with L1/2 Regularization

2018 ◽  
Vol 8 (9) ◽  
pp. 1569 ◽  
Author(s):  
Shengbing Wu ◽  
Hongkun Jiang ◽  
Haiwei Shen ◽  
Ziyi Yang
Keyword(s):  
Logistic Regression ◽  
Gene Selection ◽  
Classification Performance ◽  
Cancer Classification ◽  
Sparse Logistic Regression ◽  
The Subject ◽  
Selection For ◽  
Microarray Datasets ◽  
Sparse Methods

In recent years, gene selection for cancer classification based on the expression of a small number of gene biomarkers has been the subject of much research in genetics and molecular biology. The successful identification of gene biomarkers will help in the classification of different types of cancer and improve the prediction accuracy. Recently, regularized logistic regression using the L 1 regularization has been successfully applied in high-dimensional cancer classification to tackle both the estimation of gene coefficients and the simultaneous performance of gene selection. However, the L 1 has a biased gene selection and dose not have the oracle property. To address these problems, we investigate L 1 / 2 regularized logistic regression for gene selection in cancer classification. Experimental results on three DNA microarray datasets demonstrate that our proposed method outperforms other commonly used sparse methods ( L 1 and L E N ) in terms of classification performance.

scholarly journals Identification of TGFβ-related genes regulated in murine osteoarthritis and chondrocyte hypertrophy by comparing multiple microarray datasets

2017 ◽  
Vol 25 ◽  
pp. S211-S212
Author(s):  
L.M. de Kroon ◽  
G.G. van den Akker ◽  
B. Brachvogel ◽  
R. Narcisi ◽  
D. Belluoccio ◽  
...  
Keyword(s):  
Chondrocyte Hypertrophy ◽  
Microarray Datasets
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