Gene regulatory networks modelling using a dynamic evolutionary hybrid |
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Authors: | Ioannis A Maraziotis Andrei Dragomir Dimitris Thanos |
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Institution: | (1) Institute of Molecular Biology, Genetics and Biotechnology, Biomedical Research Foundation, Academy of Athens, 4 Soranou Efesiou Street, Athens, 11527, Greece;(2) Harrington Department of Bioengineering, Arizona State University, Tempe, AZ, USA |
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Abstract: | Background Inference of gene regulatory networks is a key goal in the quest for understanding fundamental cellular processes and revealing
underlying relations among genes. With the availability of gene expression data, computational methods aiming at regulatory
networks reconstruction are facing challenges posed by the data's high dimensionality, temporal dynamics or measurement noise.
We propose an approach based on a novel multi-layer evolutionary trained neuro-fuzzy recurrent network (ENFRN) that is able
to select potential regulators of target genes and describe their regulation type. |
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Keywords: | |
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