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pro vyhledávání: '"Surama Biswas"'
Publikováno v:
iScience, Vol 24, Iss 3, Pp 102131- (2021)
Summary: Gene regulatory networks (GRNs) process important information in developmental biology and biomedicine. A key knowledge gap concerns how their responses change over time. Hypothesizing long-term changes of dynamics induced by transient prior
Externí odkaz:
https://doaj.org/article/b5c2c96b7f374e3b9923d414f9baaece
Autor:
Surama Biswas, Sriyankar Acharyya
Publikováno v:
IEEE/ACM Transactions on Computational Biology and Bioinformatics. 18:2612-2623
Gene Regulatory Network (GRN) is formed due to mutual transcriptional regulation within a set of protein coding genes in cellular context of an organism. Computational inference of GRN is important to understand the behavior of each gene in terms of
Publikováno v:
International Journal of Molecular Sciences; Volume 24; Issue 1; Pages: 285
Trainability, in any substrate, refers to the ability to change future behavior based on past experiences. An understanding of such capacity within biological cells and tissues would enable a particularly powerful set of methods for prediction and co
Publikováno v:
IETE Journal of Research. :1-12
Reconstruction of the Gene Regulatory Network (GRN) is important in understanding the functionalities of a living cell. The reconstruction of GRN is based on microarray gene expression data. In thi...
Publikováno v:
iScience, Vol 24, Iss 3, Pp 102131-(2021)
iScience
iScience
Summary Gene regulatory networks (GRNs) process important information in developmental biology and biomedicine. A key knowledge gap concerns how their responses change over time. Hypothesizing long-term changes of dynamics induced by transient prior
Autor:
Sriyankar Acharyya, Surama Biswas
Publikováno v:
IEEE/ACM Transactions on Computational Biology and Bioinformatics. 15:2053-2059
Gene Regulatory Network (GRN) is a virtual network in a cellular context of an organism, comprising a set of genes and their internal relationships to regulate protein production rate (gene expression level) of each other through coded proteins. Comp
Autor:
Surama Biswas, Sriyankar Acharyya
Publikováno v:
CSI Transactions on ICT. 5:3-8
Identification of the genes responsible for a disease by mining gene expression data is called gene selection problem. Here, gene selection is applied to a gene expression dataset containing disease affected samples (here, a set of expression levels
Autor:
Sriyankar Acharyya, Surama Biswas
Publikováno v:
Theory in Biosciences. 135:1-19
Gene regulatory network (GRN) is produced as a result of regulatory interactions between different genes through their coded proteins in cellular context. Having immense importance in disease detection and drug finding, GRN has been modelled through
Publikováno v:
Interdisciplinary sciences, computational life sciences. 11(3)
Identifying a small subset of disease critical genes out of a large size of microarray gene expression data is a challenge in computational life sciences. This paper has applied four meta-heuristic algorithms, namely, honey bee mating optimization (H
Publikováno v:
2016 IEEE 6th International Conference on Advanced Computing (IACC).
Advent in microarray technology enables the researchers of computational biology to apply various bioinformatics paradigms on gene expression data. But microarray gene expression data, obtained as a matrix has much larger number of genes as rows than