Cancer gene silencing network analysis using cellular automata
Mahata, Kalyan and Sarkar, Anasua LU
(2015)
2015 3rd International Conference on Computer, Communication, Control and Information Technology, C3IT 2015
- Abstract
Identification of cancer pathways is the central goal in the cancer gene expression data analysis. A cellular automaton is a dynamic system with cells, which are uniform, interconnected and discrete in nature. Cellular automata are well-known methods to predict network traffics in cellular spaces. Therefore, to predict cancer pathways involved, we propose a 2-dimensional cellular automata approach over a chosen cancer gene network. Focusing on the case study, we highlight the potential impact of spatial organization in cellular spaces for the evolution and engineering of gene silencing on cancer gene expression profiles. The gene regulatory network involved in gene silencing breast cancer cell line, analysed with a predefined ranking... (More)
Identification of cancer pathways is the central goal in the cancer gene expression data analysis. A cellular automaton is a dynamic system with cells, which are uniform, interconnected and discrete in nature. Cellular automata are well-known methods to predict network traffics in cellular spaces. Therefore, to predict cancer pathways involved, we propose a 2-dimensional cellular automata approach over a chosen cancer gene network. Focusing on the case study, we highlight the potential impact of spatial organization in cellular spaces for the evolution and engineering of gene silencing on cancer gene expression profiles. The gene regulatory network involved in gene silencing breast cancer cell line, analysed with a predefined ranking value, has been simulated using cellular automata to obtain proper insight view of selecting biomarker genes for breast cancer. The predicted biomarker genes have been analysed with other contemporary databases, like KEGG and biologically tested for gene enrichment analysis for their significances. This approach is a novel one in the sense of projecting oncology in cellular spaces over ranking values for predicting significant biomarkers in cancer.
(Less)
- author
- Mahata, Kalyan
and Sarkar, Anasua
LU
- publishing date
- 2015-01-01
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- keywords
- cancer biomarker, cellular automata, Gene correlation network, gene silencing, shared neighbor ranking
- host publication
- Proceedings of the 2015 3rd International Conference on Computer, Communication, Control and Information Technology, C3IT 2015
- article number
- 7060127
- publisher
- IEEE - Institute of Electrical and Electronics Engineers Inc.
- conference name
- 2015 3rd International Conference on Computer, Communication, Control and Information Technology, C3IT 2015
- conference location
- Hooghly, India
- conference dates
- 2015-02-07 - 2015-02-08
- external identifiers
-
- scopus:84936103394
- ISBN
- 9781479944460
- DOI
- 10.1109/C3IT.2015.7060127
- language
- English
- LU publication?
- no
- id
- 837dec7d-c12d-4e5c-a9c5-b003b5ddcf37
- date added to LUP
- 2018-09-13 10:16:50
- date last changed
- 2022-01-31 05:15:38
@inproceedings{837dec7d-c12d-4e5c-a9c5-b003b5ddcf37,
abstract = {{<p>Identification of cancer pathways is the central goal in the cancer gene expression data analysis. A cellular automaton is a dynamic system with cells, which are uniform, interconnected and discrete in nature. Cellular automata are well-known methods to predict network traffics in cellular spaces. Therefore, to predict cancer pathways involved, we propose a 2-dimensional cellular automata approach over a chosen cancer gene network. Focusing on the case study, we highlight the potential impact of spatial organization in cellular spaces for the evolution and engineering of gene silencing on cancer gene expression profiles. The gene regulatory network involved in gene silencing breast cancer cell line, analysed with a predefined ranking value, has been simulated using cellular automata to obtain proper insight view of selecting biomarker genes for breast cancer. The predicted biomarker genes have been analysed with other contemporary databases, like KEGG and biologically tested for gene enrichment analysis for their significances. This approach is a novel one in the sense of projecting oncology in cellular spaces over ranking values for predicting significant biomarkers in cancer.</p>}},
author = {{Mahata, Kalyan and Sarkar, Anasua}},
booktitle = {{Proceedings of the 2015 3rd International Conference on Computer, Communication, Control and Information Technology, C3IT 2015}},
isbn = {{9781479944460}},
keywords = {{cancer biomarker; cellular automata; Gene correlation network; gene silencing; shared neighbor ranking}},
language = {{eng}},
month = {{01}},
publisher = {{IEEE - Institute of Electrical and Electronics Engineers Inc.}},
title = {{Cancer gene silencing network analysis using cellular automata}},
url = {{http://dx.doi.org/10.1109/C3IT.2015.7060127}},
doi = {{10.1109/C3IT.2015.7060127}},
year = {{2015}},
}