LEARNING GENE NETWORK USING TIME-DELAYED BAYESIAN NETWORK

2006 ◽  
Vol 15 (03) ◽  
pp. 353-370 ◽  
Author(s):  
TIE-FEI LIU ◽  
WING-KIN SUNG ◽  
ANKUSH MITTAL
Keyword(s):  
Time Delay ◽  
Gene Networks ◽  
Gene Network ◽  
Structure Learning ◽  
Learning Algorithm ◽  
Research Work ◽  
Synthetic Data ◽  
Real Data ◽  
Information Criterion

Exact determination of a gene network is required to discover the higher-order structures of an organism and to interpret its behavior. Most research work in learning gene networks either assumes that there is no time delay in gene expression or that there is a constant time delay. This paper shows how Bayesian Networks can be applied to represent multi-time delay relationships as well as directed loops. The intractability of the network learning algorithm is handled by using an improved mutual information criterion. Also, a new structure learning algorithm, "Learning By Modification", is proposed to learn the sparse structure of a gene network. The experimental results on synthetic data and real data show that our method is more accurate in determining the gene structure as compared to the traditional methods. Even transcriptional loops spanning over the whole cell can be detected by our algorithm.

Method of determination of the text direction on the image with the use of convolutional neural network

2020 ◽  
pp. 96-99
Author(s):  
P.L. Nikolaev
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Deep Neural Network ◽  
Binary Classification ◽  
Synthetic Data ◽  
Real Data ◽  
Method Of Determination ◽  
Classification Of Images

This article deals with method of binary classification of images with small text on them Classification is based on the fact that the text can have 2 directions – it can be positioned horizontally and read from left to right or it can be turned 180 degrees so the image must be rotated to read the sign. This type of text can be found on the covers of a variety of books, so in case of recognizing the covers, it is necessary first to determine the direction of the text before we will directly recognize it. The article suggests the development of a deep neural network for determination of the text position in the context of book covers recognizing. The results of training and testing of a convolutional neural network on synthetic data as well as the examples of the network functioning on the real data are presented.

scholarly journals Bayesian inversion of magnetotelluric data considering dimensionality discrepancies

2020 ◽  
Vol 223 (3) ◽  
pp. 1565-1583
Author(s):  
Hoël Seillé ◽  
Gerhard Visser
Keyword(s):  
Learning Algorithm ◽  
Synthetic Data ◽  
Real Data ◽  
Error Model ◽  
Bayesian Inversion ◽  
Magnetotelluric Data ◽  
Data Set ◽  
Likelihood Functions ◽  
Training Images ◽  
Phase Tensor

SUMMARY Bayesian inversion of magnetotelluric (MT) data is a powerful but computationally expensive approach to estimate the subsurface electrical conductivity distribution and associated uncertainty. Approximating the Earth subsurface with 1-D physics considerably speeds-up calculation of the forward problem, making the Bayesian approach tractable, but can lead to biased results when the assumption is violated. We propose a methodology to quantitatively compensate for the bias caused by the 1-D Earth assumption within a 1-D trans-dimensional Markov chain Monte Carlo sampler. Our approach determines site-specific likelihood functions which are calculated using a dimensionality discrepancy error model derived by a machine learning algorithm trained on a set of synthetic 3-D conductivity training images. This is achieved by exploiting known geometrical dimensional properties of the MT phase tensor. A complex synthetic model which mimics a sedimentary basin environment is used to illustrate the ability of our workflow to reliably estimate uncertainty in the inversion results, even in presence of strong 2-D and 3-D effects. Using this dimensionality discrepancy error model we demonstrate that on this synthetic data set the use of our workflow performs better in 80 per cent of the cases compared to the existing practice of using constant errors. Finally, our workflow is benchmarked against real data acquired in Queensland, Australia, and shows its ability to detect the depth to basement accurately.

Inferring Gene Networks using Robust Statistical Techniques

2011 ◽  
Vol 10 (1) ◽  
Author(s):  
Venkat R. Nadadoor ◽  
Amos Ben-Zvi ◽  
Sirish L. Shah
Keyword(s):  
Gene Networks ◽  
Gene Network ◽  
Information Criterion ◽  
Statistical Techniques ◽  
Connectivity Matrix ◽  
Expression Data ◽  
Statistical Tools ◽  
Linear Ordinary Differential Equations ◽  
Leave One Out ◽  
Novel Algorithm

Inference of gene networks is an important step in understanding cellular dynamics. In this work, a novel algorithm is proposed for inferring gene networks from gene expression data using linear ordinary differential equations. Under the proposed method, a combination of known statistical tools including partial least squares (PLS), leave-one-out jackknifing, and the Akaike information criterion (AIC) are used for robust estimation of gene connectivity matrix. The proposed approach is tested and validated using a computer simulated gene network model and an experimental data on a nine gene network in Eschericia coli.

Application of Bayesian Network in Learning Gene Network

2007 ◽  
pp. 319-341
Author(s):  
Tie-Fei Liu ◽  
Wing-Kin Sung ◽  
Ankush Mittal
Keyword(s):  
Mathematical Models ◽  
Bayesian Network ◽  
Gene Networks ◽  
Gene Network ◽  
Higher Order ◽  
Learning Performance ◽  
Biological Factors ◽  
Stochastic Events ◽  
Order Structures

Exact determination of a gene network is required to discover the higher-order structures of an organism and to interpret its behavior. Currently, learning gene network is one of the central themes of the post genome era. A lot of mathematical models are applied to learn gene networks. Among them, Bayesian network has shown its advantages over other methods because of its abilities to handle stochastic events, control noise, and handle dataset with a few replicates. In this chapter, we will introduce how Bayesian network has been applied to learn gene networks and how we integrated the important biological factors into the framework of Bayesian network to improve the learning performance.

scholarly journals Reverse Engineering Gene Networks Using Global-Local Shrinkage Rules

2019 ◽  
Author(s):  
Viral Panchal ◽  
Daniel Linder
Keyword(s):  
T Cell Activation ◽  
Gene Regulatory Networks ◽  
Gene Networks ◽  
Regulatory Networks ◽  
Cell Activation ◽  
Network Inference ◽  
Structure Learning ◽  
Multiple Scales ◽  
Real Data ◽  
Gene Regulatory

AbstractInferring gene regulatory networks from high-throughput ‘omics’ data has proven to be a computationally demanding task of critical importance. Frequently the classical methods breakdown due to the curse of dimensionality, and popular strategies to overcome this are typically based on regularized versions of the classical methods. However, these approaches rely on loss functions that may not be robust and usually do not allow for the incorporation of prior information in a straightforward way. Fully Bayesian methods are equipped to handle both of these shortcomings quite naturally, and they offer potential for improvements in network structure learning. We propose a Bayesian hierarchical model to reconstruct gene regulatory networks from time series gene expression data, such as those common in perturbation experiments of biological systems. The proposed methodology utilizes global-local shrinkage priors for posterior selection of regulatory edges and relaxes the common normal likelihood assumption in order to allow for heavy-tailed data, which was shown in several of the cited references to severely impact network inference. We provide a sufficient condition for posterior propriety and derive an efficient MCMC via Gibbs sampling in the Appendix. We describe a novel way to detect multiple scales based on the corresponding posterior quantities. Finally, we demonstrate the performance of our approach in a simulation study and compare it with existing methods on real data from a T-cell activation study.

scholarly journals Reverse engineering gene networks using global–local shrinkage rules

2019 ◽  
Vol 10 (1) ◽  
pp. 20190049 ◽  
Author(s):  
Viral Panchal ◽  
Daniel F. Linder
Keyword(s):  
T Cell Activation ◽  
Gene Regulatory Networks ◽  
Gene Networks ◽  
Regulatory Networks ◽  
Cell Activation ◽  
Network Inference ◽  
Structure Learning ◽  
Multiple Scales ◽  
Real Data ◽  
Gene Regulatory

Inferring gene regulatory networks from high-throughput ‘omics’ data has proven to be a computationally demanding task of critical importance. Frequently, the classical methods break down owing to the curse of dimensionality, and popular strategies to overcome this are typically based on regularized versions of the classical methods. However, these approaches rely on loss functions that may not be robust and usually do not allow for the incorporation of prior information in a straightforward way. Fully Bayesian methods are equipped to handle both of these shortcomings quite naturally, and they offer the potential for improvements in network structure learning. We propose a Bayesian hierarchical model to reconstruct gene regulatory networks from time-series gene expression data, such as those common in perturbation experiments of biological systems. The proposed methodology uses global–local shrinkage priors for posterior selection of regulatory edges and relaxes the common normal likelihood assumption in order to allow for heavy-tailed data, which were shown in several of the cited references to severely impact network inference. We provide a sufficient condition for posterior propriety and derive an efficient Markov chain Monte Carlo via Gibbs sampling in the electronic supplementary material. We describe a novel way to detect multiple scales based on the corresponding posterior quantities. Finally, we demonstrate the performance of our approach in a simulation study and compare it with existing methods on real data from a T-cell activation study.

scholarly journals A hybrid analytical–numerical algorithm for determining the neuronal current via electroencephalography

2020 ◽  
Vol 17 (163) ◽  
pp. 20190831 ◽  
Author(s):  
Parham Hashemzadeh ◽  
A. S. Fokas ◽  
C. B. Schönlieb
Keyword(s):  
Electric Potential ◽  
Focal Epilepsy ◽  
Synthetic Data ◽  
Real Data ◽  
Auxiliary Function ◽  
Alternative Approach ◽  
Eeg Data ◽  
Neuronal Current ◽  
The Given

Specific mental processes are associated with brain activation of a unique form, which are, in turn, expressed via the generation of specific neuronal electric currents. Electroencephalography (EEG) is based on measurements on the scalp of the electric potential generated by the neuronal current flowing in the cortex. This specific form of EEG data has been employed for a plethora of medical applications, from sleep studies to diagnosing focal epilepsy. In recent years, there have been efforts to use EEG data for a more ambitious purpose, namely to determine the underlying neuronal current. Although it has been known since 1853, from the studies by Helmholtz, that the knowledge of the electric potential of the external surface of a conductor is insufficient for the determination of the electric current that gave rise to this potential, the important question of which part of the current can actually be determined from the knowledge of this potential remained open until work published in 1997, when it was shown that EEG provides information only about the irrotational part of the current, which will be denoted by Ψ ; moreover, an explicit formula was derived in the above work relating this part of the current, the measured electric potential, and a certain auxiliary function, v s , that depends on the geometry of the various compartments of the brain–head system and their conductivities. In the present paper: (i) Motivated by recent results which show that, in the case of ellipsoidal geometry, the assumption of the L 2 minimization of the current yields a unique solution, we derive an analogous analytic formula characterizing this minimization for arbitrary geometry. (ii) We show that the above auxiliary function can be computed numerically via a line integral from the values of a related function v s computed via OpenMEEG; moreover, we propose an alternative approach to computing the auxiliary function v s based on the construction of a certain surrogate model. (iii) By expanding Ψ in terms of an inverse multiquadric radial basis we implement the relevant formulae numerically. The above algorithm performs well for synthetic data; its implementation with real data only requires the knowledge of the coordinates of the positions where the given EEG data are obtained.

scholarly journals An Improved Flexible Partial Histogram Bayes Learning Algorithm

2018 ◽  
Vol 11 (3) ◽  
pp. 975 ◽  
Author(s):  
Haider O. Lawend ◽  
Anuar Muad ◽  
Aini Hussain
Keyword(s):  
Supervised Classification ◽  
Nearest Neighbor ◽  
Learning Algorithm ◽  
Synthetic Data ◽  
Real Data ◽  
Gaussian Mixture ◽  
Classification Technique ◽  
Classification Tasks ◽  
Speed And Accuracy ◽  
Standard Learning

<em>This paper presents a proposed supervised classification technique namely flexible partial histogram Bayes (fPHBayes) learning algorithm. In our previous work, partial histogram Bayes (PHBayes) learning algorithm showed some advantages in the aspects of speed and accuracy in classification tasks. However, its accuracy declines when dealing with small number of instances or when the class feature distributes in wide area. In this work, the proposed fPHBayes solves these limitations in order to increase the classification accuracy. fPHBayes was analyzed and compared with PHBayes and other standard learning algorithms like first nearest neighbor, nearest subclass mean, nearest class mean, naive Bayes and Gaussian mixture model classifier. The experiments were performed using both real data and synthetic data considering different number of instances and different variances of Gaussians. The results showed that fPHBayes is more accurate and flexible to deal with different number of instances and different variances of Gaussians as compared to PHBayes.</em>

scholarly journals Learning the Graphical Structure of Electronic Health Records with Graph Convolutional Transformer

2020 ◽  
Vol 34 (01) ◽  
pp. 606-613
Author(s):  
Edward Choi ◽  
Zhen Xu ◽  
Yujia Li ◽  
Michael Dusenberry ◽  
Gerardo Flores ◽  
...  
Keyword(s):  
Electronic Health Records ◽  
Structure Learning ◽  
Learning Algorithm ◽  
Synthetic Data ◽  
Representation Learning ◽  
General Purpose ◽  
Health Records ◽  
Structure Information ◽  
Electronic Health ◽  
Graphical Structure

Effective modeling of electronic health records (EHR) is rapidly becoming an important topic in both academia and industry. A recent study showed that using the graphical structure underlying EHR data (e.g. relationship between diagnoses and treatments) improves the performance of prediction tasks such as heart failure prediction. However, EHR data do not always contain complete structure information. Moreover, when it comes to claims data, structure information is completely unavailable to begin with. Under such circumstances, can we still do better than just treating EHR data as a flat-structured bag-of-features? In this paper, we study the possibility of jointly learning the hidden structure of EHR while performing supervised prediction tasks on EHR data. Specifically, we discuss that Transformer is a suitable basis model to learn the hidden EHR structure, and propose Graph Convolutional Transformer, which uses data statistics to guide the structure learning process. The proposed model consistently outperformed previous approaches empirically, on both synthetic data and publicly available EHR data, for various prediction tasks such as graph reconstruction and readmission prediction, indicating that it can serve as an effective general-purpose representation learning algorithm for EHR data.

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