Reverse engineering module networks by PSO-RNN hybrid modeling.
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.
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TL;DR: A review of state-of-the-art techniques for analyzing omics data can be found in this article, where specific examples have facilitated and enriched analyses of sequence, transcriptomic and network data sets.
Abstract: High-throughput experimental technologies are generating increasingly massive and complex genomic data sets. The sheer enormity and heterogeneity of these data threaten to make the arising problems computationally infeasible. Fortunately, powerful algorithmic techniques lead to software that can answer important biomedical questions in practice. In this Review, we sample the algorithmic landscape, focusing on state-of-the-art techniques, the understanding of which will aid the bench biologist in analysing omics data. We spotlight specific examples that have facilitated and enriched analyses of sequence, transcriptomic and network data sets.
289 citations
01 Apr 2013
TL;DR: This Review samples the algorithmic landscape, focusing on state-of-the-art techniques, the understanding of which will aid the bench biologist in analysing omics data.
Abstract: High-throughput experimental technologies are generating increasingly massive and complex genomic data sets. The sheer enormity and heterogeneity of these data threaten to make the arising problems computationally infeasible. Fortunately, powerful algorithmic techniques lead to software that can answer important biomedical questions in practice. In this Review, we sample the algorithmic landscape, focusing on state-of-the-art techniques, the understanding of which will aid the bench biologist in analysing omics data. We spotlight specific examples that have facilitated and enriched analyses of sequence, transcriptomic and network data sets.
260 citations
TL;DR: The imputation methods are first reviewed in the context of gene expression microarray data, since most of the methods have been developed for estimating gene expression levels, then they are turned to other large-scale data sets that also suffer from the problems posed by missing values, together with pointers to possible imputation approaches in these settings.
Abstract: High-throughput biotechnologies, such as gene expression microarrays or mass-spectrometry-based proteomic assays, suffer from frequent missing values due to various experimental reasons. Since the missing data points can hinder downstream analyses, there exists a wide variety of ways in which to deal with missing values in large-scale data sets. Nowadays, it has become routine to estimate (or impute) the missing values prior to the actual data analysis. After nearly a decade since the publication of the first missing value imputation methods for gene expression microarray data, new imputation approaches are still being developed at an increasing rate. However, what is lagging behind is a systematic and objective evaluation of the strengths and weaknesses of the different approaches when faced with different types of data sets and experimental questions. In this review, the present strategies for missing value imputation and the measures for evaluating their performance are described. The imputation methods are first reviewed in the context of gene expression microarray data, since most of the methods have been developed for estimating gene expression levels; then, we turn to other large-scale data sets that also suffer from the problems posed by missing values, together with pointers to possible imputation approaches in these settings. Along with a description of the basic principles behind the different imputation approaches, the review tries to provide practical guidance for the users of high-throughput technologies on how to choose the imputation tool for their data and questions, and some additional research directions for the developers of imputation methodologies.
154 citations
Cites background from "Reverse engineering module networks..."
...It has been shown in a number of studies that missing values in large-scale microarray data sets can drastically reduce their interpretation and hinder downstream analyses, including the performance of various downstream data analysis methods, such as unsupervised clustering of genes [6, 7], detection of differentially expressed genes [8, 9], supervised classification of clinical samples [10, 11] and construction of gene regulatory networks [12, 13]....
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TL;DR: This paper presents a review of the current clustering algorithms designed especially for analyzing gene expression data and is intended to introduce one of the main problems in bioinformatics - clustering gene expressionData to the operations research community.
110 citations
TL;DR: An efficient particle swarm optimization-based path planner of an autonomous mobile robot and a fitness function has been introduced for converting the mobile robot navigation problem into multi objective optimization problem.
Abstract: While the robot is in motion, path planning should follow the three aspects: (1) acquire the knowledge from its environmental conditions. (2) determine its position in the environment and (3) decision-making and execution to achieve its highest-order goals. The present research work aims to develop an efficient particle swarm optimization-based path planner of an autonomous mobile robot. In this approach, a fitness function has been introduced for converting the mobile robot navigation problem into multi objective optimization problem. The fitness of the swarm mainly depends on two parameters: (1) distance between each particle of the swarm and target, (2) distance between each particle of the swarm and the nearest obstacle. From the obtained fitness values of the swarm, the global best position of the particle is selected in each cycle. Thereby, the robot reaches the global best position in sequence. The effectiveness of the developed algorithm in various environments has been verified by simulation modes.
58 citations
References
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06 Aug 2002
TL;DR: A concept for the optimization of nonlinear functions using particle swarm methodology is introduced, and the evolution of several paradigms is outlined, and an implementation of one of the paradigm is discussed.
Abstract: A concept for the optimization of nonlinear functions using particle swarm methodology is introduced. The evolution of several paradigms is outlined, and an implementation of one of the paradigms is discussed. Benchmark testing of the paradigm is described, and applications, including nonlinear function optimization and neural network training, are proposed. The relationships between particle swarm optimization and both artificial life and genetic algorithms are described.
35,104 citations
TL;DR: A comprehensive catalog of yeast genes whose transcript levels vary periodically within the cell cycle is created, and it is found that the mRNA levels of more than half of these 800 genes respond to one or both of these cyclins.
Abstract: We sought to create a comprehensive catalog of yeast genes whose transcript levels vary periodically within the cell cycle. To this end, we used DNA microarrays and samples from yeast cultures sync...
5,176 citations
"Reverse engineering module networks..." refers background or methods in this paper
...[17] assigned attributes (called peaks) for genes that represent the time when gene expression levels take the peak during cell cycle....
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...Yeast cell cycle data The yeast cell cycle data presented in [17] consist of six time series (cln3, clb2, alpha, cdc15, cdc28, and elu) expression measurements of the transcript (mRNA) levels of S....
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...Both data were preprocessed in the original studies [16,17]....
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...800 genes were identified as cell cycle regulated based on cluster analysis in [17]....
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01 Jan 1990
TL;DR: This paper first reviews basic backpropagation, a simple method which is now being widely used in areas like pattern recognition and fault diagnosis, and describes further extensions of this method, to deal with systems other than neural networks, systems involving simultaneous equations or true recurrent networks, and other practical issues which arise with this method.
Abstract: Basic backpropagation, which is a simple method now being widely used in areas like pattern recognition and fault diagnosis, is reviewed. The basic equations for backpropagation through time, and applications to areas like pattern recognition involving dynamic systems, systems identification, and control are discussed. Further extensions of this method, to deal with systems other than neural networks, systems involving simultaneous equations, or true recurrent networks, and other practical issues arising with the method are described. Pseudocode is provided to clarify the algorithms. The chain rule for ordered derivatives-the theorem which underlies backpropagation-is briefly discussed. The focus is on designing a simpler version of backpropagation which can be translated into computer code and applied directly by neutral network users. >
4,572 citations
"Reverse engineering module networks..." refers background in this paper
...BPTT is an extension of the standard back-propagation algorithm, using gradient descent method to find the best solution....
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...There exist many algorithms for RNN training in the literature, e.g., back-propagation through time (BPTT) [27] and genetic algorithm (GA) [12]....
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..., back-propagation through time (BPTT) [27] and genetic algorithm (GA) [12]....
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TL;DR: In this paper, the authors proposed a method called the "gap statistic" for estimating the number of clusters (groups) in a set of data, which uses the output of any clustering algorithm (e.g. K-means or hierarchical), comparing the change in within-cluster dispersion with that expected under an appropriate reference null distribution.
Abstract: We propose a method (the ‘gap statistic’) for estimating the number of clusters (groups) in a set of data. The technique uses the output of any clustering algorithm (e.g. K-means or hierarchical), comparing the change in within-cluster dispersion with that expected under an appropriate reference null distribution. Some theory is developed for the proposal and a simulation study shows that the gap statistic usually outperforms other methods that have been proposed in the literature.
4,283 citations
Book•
01 Jan 2000
TL;DR: The gap statistic is proposed for estimating the number of clusters (groups) in a set of data by comparing the change in within‐cluster dispersion with that expected under an appropriate reference null distribution.
Abstract: We propose a method (the ‘gap statistic’) for estimating the number of clusters (groups) in a set of data. The technique uses the output of any clustering algorithm (e.g. K-means or hierarchical), comparing the change in within-cluster dispersion with that expected under an appropriate reference null distribution. Some theory is developed for the proposal and a simulation study shows that the gap statistic usually outperforms other methods that have been proposed in the literature.
3,860 citations
Additional excerpts
...Xie-Beni statistic [24]and gap statistic [25]....
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