Neural model of gene regulatory network: a survey on supportive meta-heuristics
| Autor: | Sriyankar Acharyya, Surama Biswas |
|---|---|
| Rok vydání: | 2016 |
| Předmět: |
0301 basic medicine
Statistics and Probability Computer science 0206 medical engineering Complex system Gene regulatory network Context (language use) 02 engineering and technology Machine learning computer.software_genre Bioinformatics 03 medical and health sciences Animals Heuristics Humans Computer Simulation Gene Regulatory Networks Metaheuristic Ecology Evolution Behavior and Systematics Models Genetic Artificial neural network business.industry Applied Mathematics Computational Biology Models Theoretical Weighting ComputingMethodologies_PATTERNRECOGNITION 030104 developmental biology Recurrent neural network Neural Networks Computer ComputingMethodologies_GENERAL Artificial intelligence business computer Algorithms 020602 bioinformatics |
| Zdroj: | Theory in Biosciences. 135:1-19 |
| ISSN: | 1611-7530 1431-7613 |
| DOI: | 10.1007/s12064-016-0224-z |
| Popis: | 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 various mathematical and computational schemes and reported in survey articles. Neural and neuro-fuzzy models have been the focus of attraction in bioinformatics. Predominant use of meta-heuristic algorithms in training neural models has proved its excellence. Considering these facts, this paper is organized to survey neural modelling schemes of GRN and the efficacy of meta-heuristic algorithms towards parameter learning (i.e. weighting connections) within the model. This survey paper renders two different structure-related approaches to infer GRN which are global structure approach and substructure approach. It also describes two neural modelling schemes, such as artificial neural network/recurrent neural network based modelling and neuro-fuzzy modelling. The meta-heuristic algorithms applied so far to learn the structure and parameters of neutrally modelled GRN have been reviewed here. |
| Databáze: | OpenAIRE |
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