Inferring network interactions using recurrent neural networks and swarm intelligence.
01 Jan 2006-Vol. 1, pp 4241-4244
TL;DR: The results indicate that the proposed hybrid SI-RNN algorithm has a promising potential to infer complex interactions such as gene regulatory networks from time-series gene expression data.
Abstract: We present a novel algorithm combining artificial neural networks and swarm intelligence (SI) methods to infer network interactions The algorithm uses ant colony optimization (ACO) to identify the optimal architecture of a recurrent neural network (RNN), while the weights of the RNN are optimized using particle swarm optimization (PSO) Our goal is to construct an RNN that mimics the true structure of an unknown network and the time-series data that the network generated We applied the proposed hybrid SI-RNN algorithm to infer a simulated genetic network The results indicate that the algorithm has a promising potential to infer complex interactions such as gene regulatory networks from time-series gene expression data I INTRODUCTION inference algorithm based on GAs for the optimization of the influence matrix of gene regulatory network In (13), GAs and ANNs are combined to determine gene interactions in temporal gene expression data In this paper, we propose to apply a hybrid of ANNs and swarm intelligence (SI) methods (12) to infer network interactions from time-series data The architecture and the synaptic weights of a recurrent neural network (RNN) are optimized using ant colony optimization (ACO) and particle swarm optimization (PSO) methods, respectively Unlike previous computational methods, which targeted at one-step- ahead prediction of time-series data (13), our method enables a multi-step-ahead prediction This is achieved through our RNN, which is self-evolutionary The RNN starts with a given initial condition, evolves, and eventually reaches final states The proposed hybrid SI-RNN algorithm selects the architecture of the RNN and weights not only to mimic the response of the unknown network at each time point but also to identify the structure of the network that generated the time-series data This is a challenging task given that there may be many possible structures with responses that closely match the generated data The algorithm evaluates various structures through the cross- validation method to avoid the selection of a wrong structure and to make sure that the correct structure is identified despite the presence of noise and complexity of the unknown network We successfully applied the algorithm to infer simulated network interactions
Citations
More filters
TL;DR: This review provides an introduction to current CI methods, their application to biological problems, and concludes with a commentary about the anticipated impact of these approaches in bioinformatics.
Abstract: Biology, chemistry and medicine are faced by tremendous challenges caused by an overwhelming amount of data and the need for rapid interpretation. Computational intelligence (CI) approaches such as artificial neural networks, fuzzy systems and evolutionary computation are being used with increasing frequency to contend with this problem, in light of noise, non-linearity and temporal dynamics in the data. Such methods can be used to develop robust models of processes either on their own or in combination with standard statistical approaches. This is especially true for database mining, where modeling is a key component of scientific understanding. This review provides an introduction to current CI methods, their application to biological problems, and concludes with a commentary about the anticipated impact of these approaches in bioinformatics.
68Â citations
TL;DR: This study presents a novel GRN inference method by integrating gene expression data and gene functional category information that is shown to lead to biologically meaningful modules and networks among the modules.
Abstract: Background
Inferring a gene regulatory network (GRN) from high throughput biological data is often an under-determined problem and is a challenging task due to the following reasons: (1) thousands of genes are involved in one living cell; (2) complex dynamic and nonlinear relationships exist among genes; (3) a substantial amount of noise is involved in the data, and (4) the typical small sample size is very small compared to the number of genes. We hypothesize we can enhance our understanding of gene interactions in important biological processes (differentiation, cell cycle, and development, etc) and improve the inference accuracy of a GRN by (1) incorporating prior biological knowledge into the inference scheme, (2) integrating multiple biological data sources, and (3) decomposing the inference problem into smaller network modules.
51Â citations
TL;DR: A computational framework for utilizing data from multiple sources to infer TF-target gene relationships on the basis of NMs is introduced, useful for inferring small NM-based modules of TF- target gene relationships that can serve as a basis for generating new testable hypotheses.
Abstract: Integrating data from multiple global assays and curated databases is essential to understand the spatio-temporal interactions within cells. Different experiments measure cellular processes at various widths and depths, while databases contain biological information based on established facts or published data. Integrating these complementary datasets helps infer a mutually consistent transcriptional regulatory network (TRN) with strong similarity to the structure of the underlying genetic regulatory modules. Decomposing the TRN into a small set of recurring regulatory patterns, called network motifs (NM), facilitates the inference. Identifying NMs defined by specific transcription factors (TF) establishes the framework structure of a TRN and allows the inference of TF-target gene relationship. This paper introduces a computational framework for utilizing data from multiple sources to infer TF-target gene relationships on the basis of NMs. The data include time course gene expression profiles, genome-wide location analysis data, binding sequence data, and gene ontology (GO) information. The proposed computational framework was tested using gene expression data associated with cell cycle progression in yeast. Among 800 cell cycle related genes, 85 were identified as candidate TFs and classified into four previously defined NMs. The NMs for a subset of TFs are obtained from literature. Support vector machine (SVM) classifiers were used to estimate NMs for the remaining TFs. The potential downstream target genes for the TFs were clustered into 34 biologically significant groups. The relationships between TFs and potential target gene clusters were examined by training recurrent neural networks whose topologies mimic the NMs to which the TFs are classified. The identified relationships between TFs and gene clusters were evaluated using the following biological validation and statistical analyses: (1) Gene set enrichment analysis (GSEA) to evaluate the clustering results; (2) Leave-one-out cross-validation (LOOCV) to ensure that the SVM classifiers assign TFs to NM categories with high confidence; (3) Binding site enrichment analysis (BSEA) to determine enrichment of the gene clusters for the cognate binding sites of their predicted TFs; (4) Comparison with previously reported results in the literatures to confirm the inferred regulations. The major contribution of this study is the development of a computational framework to assist the inference of TRN by integrating heterogeneous data from multiple sources and by decomposing a TRN into NM-based modules. The inference capability of the proposed framework is verified statistically (e.g., LOOCV) and biologically (e.g., GSEA, BSEA, and literature validation). The proposed framework is useful for inferring small NM-based modules of TF-target gene relationships that can serve as a basis for generating new testable hypotheses.
45Â citations
Cites methods from "Inferring network interactions usin..."
...Due to its capability to capture the nonlinear properties and dynamic relationships, RNNs have been previously applied for GRN inference [33,50,51]....
[...]
TL;DR: An integrative framework that infers gene regulatory modules from the cell cycle of cancer cells by incorporating multiple sources of biological data, including gene expression profiles, gene ontology, and molecular interaction is proposed.
Abstract: Background
Precise regulation of the cell cycle is crucial to the growth and development of all organisms. Understanding the regulatory mechanism of the cell cycle is crucial to unraveling many complicated diseases, most notably cancer. Multiple sources of biological data are available to study the dynamic interactions among many genes that are related to the cancer cell cycle. Integrating these informative and complementary data sources can help to infer a mutually consistent gene transcriptional regulatory network with strong similarity to the underlying gene regulatory relationships in cancer cells.
23Â citations
Cites methods from "Inferring network interactions usin..."
...The PSO generation for RNN was set to 1000 [39]....
[...]
...Due to its capability to capture the nonlinear properties and dynamic relationships, RNNs have been applied for TRN inference [39,55,56]....
[...]
References
More filters
TL;DR: This paper proposes a method to capture the dynamics in gene expression data using S-system formalism and construct genetic network models using the probabilistic heuristic search and divide-and-conquer approach to estimate the network structure.
Abstract: This paper proposes a method to capture the dynamics in gene expression data using S-system formalism and construct genetic network models. Our purposed method exploits the probabilistic heuristic search and divide-and-conquer approach to estimate the network structure. In evaluating the network structure, we attempt a primitive integration of other knowledge to the statistical criterion. The Z-score is used to analyze the robust and signican t parameters from stochastic search results. We evaluated the proposed method on articially generated data and E.coli mRNA expression data.
40Â citations
"Inferring network interactions usin..." refers background in this paper
...Genetic algorithms (GAs) have also been applied to decipher genetic networks from gene expression data [ 9-11 ]....
[...]
...Genetic algorithms (GAs) have also been applied to decipher genetic networks from gene expression data [9-11]. Shin and Iba [ 11 ] developed an...
[...]
TL;DR: Artificial neural networks of the present type, with fully interconnected feedforward architectures and trained according to the backpropagation algorithm, scaled poorly as the problem size was increased.
Abstract: Here we develop the use of artificial neural networks for solving the inverse metabolic problem, in other words, given a set of steady-state metabolite levels and fluxes in a pathway of known structure to obtain the parameters of the system, in this case the enzymatic limiting rate and Michaelis constants. This requires two main procedures: first the development of a computer program with which one can model metabolism in the forward direction (i.e. given the internal and parameters to determine the steady-state fluxes and metabolite concentrations), and second, given arrays of associated parameters and variables thereby obtained, to exploit artificial neural networks to form a model capable of obtaining the parameters from the variables. We studied 2-step pathways exhibiting first-order kinetics, 2-step pathways exhibiting reversible Michaelis-Menten kinetics and then 3-step pathways (again exhibiting reversible Michaelis-Menten kinetics), modelled using the program Gepasi. Whilst it was fairly easy for the networks to learn most of the parameters in the 2-step pathway, it was found helpful for the Michaelis-Menten case to vary the concentration of the starting pathway substrate for each set of internal parameters, and to train separate networks for each parameter. Some parameters were much easier to learn than others, reverse Km and Vmax values normally being the most difficult. For the 3-step pathway learning sometimes required as much as 3 days, and occasionally convergence was not obtained. Overall, neural networks of the present type, with fully interconnected feedforward architectures and trained according to the backpropagation algorithm, scaled poorly as the problem size was increased.
27Â citations
"Inferring network interactions usin..." refers methods in this paper
...In [ 7 ], ANNs are developed for solving the inverse metabolic problem....
[...]
TL;DR: In this paper, the inference of the interactions in more large scale of gene expression networks is attempted, and new efficient approaches to narrow down the candidates that explain the observed time-courses within the immense huge searching space of parameter values are proposed.
Abstract: Recent advances of powerful new technologies such as DNA microarrays provide a mass of gene expression data on a genomic scale. One of the most important projects in post-genome-era is the system identification of gene networks by using these observed data. We previously introduced an efficient numerical optimization technique by using time-course data of system components, which is based on real-coded genetic algorithm (RCGAs) to estimate the reaction coefficients among system components of a dynamic network model called S-system [3] that is a type of power-low formalism and is suitable for description of organizationally complex systems such as gene expression networks and metabolic pathways. This technique with the combination of one of the crossover operators for RCGAs called unimodal normal distribution crossover (UNDX) [1] with the alternation of generation model called minimal generation gap (MGG) [2] showed remarkable superiority to the simple GA in case of simple oscillatory system [4]. However this case study belongs to a comparative easy inverse problem; the number of system components was 2 and the estimated parameters was 12. For application to gene networks including huge number of estimated parameters, our new optimization techniques have to be adapted to inverse problem with more strict circumstances. In this paper, we shall attempt to the inference of the interactions in more large scale of gene expression networks. In the case study, we also propose new efficient approaches to narrow down the candidates that explain the observed time-courses within the immense huge searching space of parameter values.
15Â citations
"Inferring network interactions usin..." refers background in this paper
...Genetic algorithms (GAs) have also been applied to decipher genetic networks from gene expression data [ 9-11 ]....
[...]