State of the Art of Fuzzy Methods for Gene Regulatory Networks Inference
| Autor: | Aboubekeur Hamdi-Cherif, Chafia Kara-Mohamed, Tuqyah Abdullah Al Qazlan |
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| Rok vydání: | 2015 |
| Předmět: |
Gene regulatory network
lcsh:Medicine Inference Throughput Review Article Biology computer.software_genre lcsh:Technology Fuzzy logic General Biochemistry Genetics and Molecular Biology Field (computer science) Fuzzy Logic Multidisciplinary approach Animals Humans Gene Regulatory Networks lcsh:Science General Environmental Science Models Genetic lcsh:T lcsh:R Computational Biology General Medicine Fuzzy control system Data science lcsh:Q State (computer science) Data mining computer |
| Zdroj: | The Scientific World Journal, Vol 2015 (2015) The Scientific World Journal |
| ISSN: | 1537-744X 2356-6140 |
| Popis: | To address one of the most challenging issues at the cellular level, this paper surveys the fuzzy methods used in gene regulatory networks (GRNs) inference. GRNs represent causal relationships between genes that have a direct influence, trough protein production, on the life and the development of living organisms and provide a useful contribution to the understanding of the cellular functions as well as the mechanisms of diseases. Fuzzy systems are based on handling imprecise knowledge, such as biological information. They provide viable computational tools for inferring GRNs from gene expression data, thus contributing to the discovery of gene interactions responsible for specific diseases and/orad hoccorrecting therapies. Increasing computational power and high throughput technologies have provided powerful means to manage these challenging digital ecosystems at different levels from cell to society globally. The main aim of this paper is to report, present, and discuss the main contributions of this multidisciplinary field in a coherent and structured framework. |
| Databáze: | OpenAIRE |
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