Reconstruction of Gene Regulatory Networks from Gene Expression Data Using Decoupled Recurrent Neural Network Model

Noman, Nasimul; Palafox, Leon; Iba, Hitoshi

doi:10.1007/978-4-431-54394-7_8

Cited by 39 publications

(49 citation statements)

References 23 publications

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“…These techniques can be clubbed into different groups, such as Boolean networks (Liang et al, 1998;Akutsu et al, 1999;Shmulevich et al, 2002;Martin et al, 2007;Raza and Jaiswal, 2013;Raza and Parveen, 2013), Bayesian networks (Friedman et al, 2000;Husmeier, 2003), Petri nets (Koch et al, 2005;Remy et al, 2006), linear and non-linear ordinary differential equations (ODEs) (Chen et al, 1999;Tyson et al, 2002;De Jong and Page, 2008), machine learning approaches (Weaver et al, 1999;Kim et al, 2000;Vohradský, 2001;Keedwell et al, 2002;Huang et al, 2003;Tian and Burrage, 2003;Zhou et al, 2004;Xu et al, 2004;Hu et al, 2006;Jung and Cho, 2007;Xu et al, 2007a,b;Chiang and Chao, 2007;Lee and Yang, 2008;Datta et al, 2009;Zhang et al, 2009;Maraziotis et al, 2010;Ghazikhani et al, 2011;Liu et al, 2011;Kentzoglanakis and Poole, 2012;Noman et al, 2013), etc.…”

Section: Finding Regulatory Relationship Among Genesmentioning

confidence: 99%

“…In this model, time delay between the output of a gene and its effect on another gene has been incorporated. A more recent work by Noman and his colleagues (Noman et al, 2013) proposed a decoupled-RNN model of GRN. Here, decoupled means dividing the estimation problem of parameters for the complete network into several sub-problems, each of which estimate parameters associated with single gene.…”

Section: Artificial Neural Networkmentioning

confidence: 99%

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Analysis of Microarray Data using Artificial Intelligence Based Techniques

Raza

2016

Handbook of Research on Computational Intelligence Applications in Bioinformatics

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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.

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Section: Finding Regulatory Relationship Among Genesmentioning

confidence: 99%

Section: Artificial Neural Networkmentioning

confidence: 99%

Analysis of Microarray Data using Artificial Intelligence Based Techniques

Raza

2016

Handbook of Research on Computational Intelligence Applications in Bioinformatics

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“…In RNNs, connections between nodes (genes or proteins) form a directed cycle and therefore can have very different dynamics from the standard feedforward networks. This approach has recently been used to infer genetic networks (40, 41). Here, we will use a similar, yet more refined, approach to study key signaling pathways in melanoma (skin cancer).…”

Section: Pathway Activity In Normal and Cancer Cellsmentioning

confidence: 99%

Bridging scales in cancer progression: Mapping genotype to phenotype using neural networks

Gerlee

Kim

Anderson

2015

Seminars in Cancer Biology

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In this review we summarize our recent efforts in trying to understand the role of heterogeneity in cancer progression by using neural networks to characterise different aspects of the mapping from a cancer cells genotype and environment to its phenotype. Our central premise is that cancer is an evolving system subject to mutation and selection, and the primary conduit for these processes to occur is the cancer cell whose behaviour is regulated on multiple biological scales. The selection pressure is mainly driven by the microenvironment that the tumour is growing in and this acts directly upon the cell phenotype. In turn, the phenotype is driven by the intracellular pathways that are regulated by the genotype. Integrating all of these processes is a massive undertaking and requires bridging many biological scales (i.e. genotype, pathway, phenotype and environment) that we will only scratch the surface of in this review. We will focus on models that use neural networks as a means of connecting these different biological scales, since they allow us to easily create heterogeneity for selection to act upon and importantly this heterogeneity can be implemented at different biological scales. More specifically, we consider three different neural networks that bridge different aspects of these scales and the dialogue with the micro-environment, (i) the impact of the microenvironment on evolutionary dynamics, (ii) the mapping from genotype to phenotype under drug-induced perturbations and (iii) pathway activity in both normal and cancer cells under different micro-environmental conditions.

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“…The error in prediction is calculated and weights are updated [64]. A particular type of ANN called Recurrent Neural Network (RNN) has been developed to find out gene regulatory networks in time series RNA expression data [65], [66], wherein positive and negative feedback loops are considered on the internal states. The RNNs have significant characteristics to make it computationally feasible (e.g.…”

Section: Artificial Neural Networkmentioning

confidence: 99%

Systems Genetics of Complex Diseases Using RNA-Sequencing Methods

Mazzoni¹,

Kogelman²,

Suravajhala³

et al. 2015

IJBBB

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Next generation sequencing technologies have enabled the generation of huge quantities of biological data, and nowadays extensive datasets at different 'omics levels have been generated. Systems genetics is a powerful approach that allows to integrate different 'omics level and understand the biological mechanisms behind complex diseases or traits. In the recent, transcriptomic studies with microarrays have been replaced with the new powerful RNA-seq technologies. This has led to detection of novel gene transcripts, novel regulatory mechanisms, allele specific gene expression and numerous non-coding RNAs (ncRNAs). The integration of transcriptomics data with genomic data in a systems genetics context represents a valuable possibility to go deep into the causal and regulatory mechanisms that generate complex traits and diseases. However RNA-Seq data have to be treated carefully and the choice of the right methodology could have a great impact on the final results. Furthermore the integration of different level is not trivial. Here we give a comprehensive systems genetics overview of the methods and tools for analysis and the integration of RNA-Seq data including ncRNAs. We focused principally on merits and demerits of tools for post mapping quality control, normalization, differential expression analysis, gene network analysis, and integration of different omics data in order to generate a comprehensive guideline to systems genetics analysis using RNA-Seq data.

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Reconstruction of Gene Regulatory Networks from Gene Expression Data Using Decoupled Recurrent Neural Network Model

Cited by 39 publications

References 23 publications

Analysis of Microarray Data using Artificial Intelligence Based Techniques

Analysis of Microarray Data using Artificial Intelligence Based Techniques

Bridging scales in cancer progression: Mapping genotype to phenotype using neural networks

Systems Genetics of Complex Diseases Using RNA-Sequencing Methods

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