Proceedings ArticleDOI
Genetic Regulatory Network Inference using Recurrent Neural Networks trained by a Multi Agent System
Adel Ghazikhani,T. Mohammad Reza Akbarzadeh,Reza Monsefi +2 more
- pp 95-99
TLDR
A novel algorithm for gene regulatory network inference that uses RNN with standard PSO for training and the results show improvements using the E. coli SOS dataset.Abstract:
We propose a novel algorithm for gene regulatory network inference. Gene Regulatory Network (GRN) inference is approximating the combined effect of different genes in a specific genome data. GRNs are nonlinear, dynamic and noisy. Timeseries data has been frequently used for GRN modeling. Due to the function approximation and feedback nature of GRN, a Recurrent Neural Network (RNN) model is used. RNN training is a complicated task. We propose a multi agent system for RNN training. The agents of the proposed multi agent system trainer are separate swarms of particles building up a multi population Particle Swarm Optimization (PSO) algorithm. We compare the proposed algorithm with a similar algorithm that uses RNN with standard PSO for training. The results show improvements using the E. coli SOS dataset. KeywordsGene Regulatory Network Inference, Particle Swarm Optimization, Multi Population PSO, Recurrent Neural Networks, Multi Agent Systemsread more
Citations
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Journal ArticleDOI
Integrative approaches to reconstruct regulatory networks from multi-omics data: A review of state-of-the-art methods.
Nisar Wani,Khalid Raza +1 more
TL;DR: In this paper, the authors present a survey of integration methods that reconstruct regulatory networks using state-of-the-art techniques to handle multi-omics (i.e., genomic, transcriptomic, proteomic) and other biological datasets.
Journal ArticleDOI
Recurrent neural network based hybrid model for reconstructing gene regulatory network
Khalid Raza,Mansaf Alam +1 more
TL;DR: In this paper, the authors proposed a recurrent neural network (RNN) based hybrid model of gene regulatory network (GRN), which is able to capture complex, non-linear and dynamic relationships among variables.
Journal ArticleDOI
Recurrent Neural Network Based Hybrid Model of Gene Regulatory Network.
TL;DR: A recurrent neural network (RNN) based model of GRN, hybridized with generalized extended Kalman filter for weight update in backpropagation through time training algorithm, and a comparison of the results with other state-of-the-art techniques shows superiority of the proposed model.
Book ChapterDOI
Analysis of Microarray Data Using Artificial Intelligence Based Techniques
TL;DR: Artificial intelligence based techniques for the analysis of microarray gene expression data are reviewed and challenges in the field and future work direction have also been suggested.
Journal ArticleDOI
A guide to gene regulatory network inference for obtaining predictive solutions: Underlying assumptions and fundamental biological and data constraints
TL;DR: Given the intrinsic interdisciplinary nature of gene regulatory network inference, this work presents a review on the currently available approaches, their challenges and limitations and proposes guidelines to select the most appropriate method considering the underlying assumptions and fundamental biological and data constraints.
References
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Journal ArticleDOI
Gene networks inference using dynamic Bayesian networks
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TL;DR: This article deals with the identification of gene regulatory networks from experimental data using a statistical machine learning approach that can be described as a dynamic Bayesian network particularly well suited to tackle the stochastic nature of gene regulation and gene expression measurement.