T. Mohammad Reza Akbarzadeh
Bio: T. Mohammad Reza Akbarzadeh is an academic researcher from Ferdowsi University of Mashhad. The author has contributed to research in topics: Recurrent neural network & Adaptive neuro fuzzy inference system. The author has an hindex of 1, co-authored 2 publications receiving 11 citations.
13 Oct 2011
TL;DR: 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 Systems
••04 Feb 2014
TL;DR: Rule-based classification with Neural Networks has high acceptance ability for noisy data, high accuracy and is preferable in data mining, and this paper uses Fuzzy Min-Max (FMM) Neural Network, which uses modified GA to minimize the number of features in the extracted rules.
Abstract: Rule-based classification with Neural Networks has high acceptance ability for noisy data, high accuracy and is preferable in data mining. In this paper, we use Fuzzy Min-Max (FMM) Neural Network. Nevertheless the - Curse of Dimensionality - problem also exists in this classifier. As a possible solution, in this paper the modified GA is adopted to minimize the number of features in the extracted rules. “Guided Elitism” strategy is used to create elitism in the population, based on information extracted from good individuals of previous generations. The main advantage of this data structure is that it maintains partial information of good solutions, which may otherwise be lost in the selection process. Five well-known benchmark problems are used to evaluate the performance of the proposed GEGA system; Results shows comparatively high accuracy and generally lower computational time.
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.
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.
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.
Abstract: Systems biology is an emerging interdisciplinary area of research that focuses on study of complex interactions in a biological system, such as gene regulatory networks. The discovery of gene regulatory networks leads to a wide range of applications, such as pathways related to a disease that can unveil in what way the disease acts and provide novel tentative drug targets. In addition, the development of biological models from discovered networks or pathways can help to predict the responses to disease and can be much useful for the novel drug development and treatments. The inference of regulatory networks from biological data is still in its infancy stage. This paper proposes a recurrent neural network (RNN) based gene regulatory network (GRN) model hybridized with generalized extended Kalman filter for weight update in backpropagation through time training algorithm. The RNN is a complex neural network that gives a better settlement between the biological closeness and mathematical flexibility to model GRN. The RNN is able to capture complex, non-linear and dynamic relationship among variables. Gene expression data are inherently noisy and Kalman filter performs well for estimation even in noisy data. Hence, non-linear version of Kalman filter, i.e., generalized extended Kalman filter has been applied for weight update during network training. The developed model has been applied on DNA SOS repair network, IRMA network, and two synthetic networks from DREAM Challenge. We compared our results with other state-of-the-art techniques that show superiority of our model. Further, 5% Gaussian noise has been added in the dataset and result of the proposed model shows negligible effect of noise on the results.
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.
Abstract: Microarray is one of the essential technologies used by the biologist to measure genome-wide expression levels of genes in a particular organism under some particular conditions or stimuli. As microarrays technologies have become more prevalent, the challenges of analyzing these data for getting better insight about biological processes have essentially increased. Due to availability of artificial intelligence based sophisticated computational techniques, such as artificial neural networks, fuzzy logic, genetic algorithms, and many other nature-inspired algorithms, it is possible to analyse microarray gene expression data in more better way. Here, we reviewed artificial intelligence based techniques for the analysis of microarray gene expression data. Further, challenges in the field and future work direction have also been suggested.
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.
Abstract: The study of biological systems at a system level has become a reality due to the increasing powerful computational approaches able to handle increasingly larger datasets. Uncovering the dynamic nature of gene regulatory networks in order to attain a system level understanding and improve the predictive power of biological models is an important research field in systems biology. The task itself presents several challenges, since the problem is of combinatorial nature and highly depends on several biological constraints and also the intended application. Given the intrinsic interdisciplinary nature of gene regulatory network inference, we present a review on the currently available approaches, their challenges and limitations. We propose guidelines to select the most appropriate method considering the underlying assumptions and fundamental biological and data constraints.