Gene expression prediction using deep neural networks
Joint Authors
Source
The International Arab Journal of Information Technology
Issue
Vol. 17, Issue 3 (31 May. 2020), pp.422-431, 10 p.
Publisher
Zarqa University Deanship of Scientific Research
Publication Date
2020-05-31
Country of Publication
Jordan
No. of Pages
10
Main Subjects
Information Technology and Computer Science
Topics
Abstract EN
In the field of molecular biology, gene expression is a term that encompasses all the information contained in an organism’s genome.
Although, researchers have developed several clinical techniques to quantitatively measure the expressions of genes of an organism, they are too costly to be extensively used.
The NIH LINCS program revealed that human gene expressions are highly correlated.
Further research at the University of California, Irvine (UCI) led to the development of D-GEX, a Multi Layer Perceptron (MLP) model that was trained to predict unknown target expressions from previously identified landmark expressions.
But, bowing to hardware limitations, they had split the target genes into different sets and constructed separate models to profile the whole genome.
This paper proposes an alternative solution using a combination of deep autoencoder and MLP to overcome this bottleneck and improve the prediction performance.
The microarray based Gene Expression Omnibus (GEO) dataset was employed to train the neural networks.
Experimental result shows that this new model, abbreviated as E-GEX, outperforms D-GEX by 16.64% in terms of overall prediction accuracy on GEO dataset.
The models were further tested on an RNA-Seq based 1000G dataset and E-GEX was found to be 49.23% more accurate than D-GEX.
American Psychological Association (APA)
Bhukya, Raju& Ashok, Achyuth. 2020. Gene expression prediction using deep neural networks. The International Arab Journal of Information Technology،Vol. 17, no. 3, pp.422-431.
https://search.emarefa.net/detail/BIM-962357
Modern Language Association (MLA)
Bhukya, Raju& Ashok, Achyuth. Gene expression prediction using deep neural networks. The International Arab Journal of Information Technology Vol. 17, no. 3 (May. 2020), pp.422-431.
https://search.emarefa.net/detail/BIM-962357
American Medical Association (AMA)
Bhukya, Raju& Ashok, Achyuth. Gene expression prediction using deep neural networks. The International Arab Journal of Information Technology. 2020. Vol. 17, no. 3, pp.422-431.
https://search.emarefa.net/detail/BIM-962357
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references : p. 430-431
Record ID
BIM-962357