Improved inference of chromosome conformation from images of labeled loci

Reconstruction Accuracy ◽

Chromosome Conformation ◽

Imaging Technologies ◽

Link Type ◽

We previously published a method that infers chromosome conformation from images of fluorescently-tagged genomic loci, for the case when there are many loci labeled with each distinguishable color. Here we build on our previous work and improve the reconstruction algorithm to address previous limitations. We show that these improvements 1) increase the reconstruction accuracy and 2) allow the method to be used on large-scale problems involving several hundred labeled loci. Simulations indicate that full-chromosome reconstructions at 1/2 Mb resolution are possible using existing labeling and imaging technologies. The updated reconstruction code and the script files used for this paper are available at: https://github.com/heltilda/align3d.

Improved inference of chromosome conformation from images of labeled loci

10.1101/409847 ◽

2018 ◽

Author(s):

Brian C. Ross ◽

James Costello

Keyword(s):

Large Scale ◽

Reconstruction Accuracy ◽

Chromosome Conformation ◽

Imaging Technologies ◽

Link Type ◽

AbstractWe previously published a method that infers chromosome conformation from images of fluorescently-tagged genomic loci, for the case when there are many loci labeled with each distinguishable color. Here we build on our previous work and improve the reconstruction algorithm to address previous limitations. We show that these improvements 1) increase the reconstruction accuracy and 2) allow the method to be used on large-scale problems involving several hundred labeled loci. Simulations indicate that full-chromosome reconstructions at 1/2 Mb resolution are possible using existing labeling and imaging technologies. The updated reconstruction code and the script files used for this paper are available at: https://github.com/heltilda/align3d.

Improved inference of chromosome conformation from images of labeled loci

F1000Research ◽

10.12688/f1000research.16252.3 ◽

2019 ◽

Vol 7 ◽

pp. 1521

Author(s):

Brian C. Ross ◽

James C. Costello

Keyword(s):

Large Scale ◽

Reconstruction Accuracy ◽

Chromosome Conformation ◽

Imaging Technologies ◽

Link Type ◽

We previously published a method that infers chromosome conformation from images of fluorescently-tagged genomic loci, for the case when there are many loci labeled with each distinguishable color. Here we build on our previous work and improve the reconstruction algorithm to address previous limitations. We show that these improvements 1) increase the reconstruction accuracy and 2) allow the method to be used on large-scale problems involving several hundred labeled loci. Simulations indicate that full-chromosome reconstructions at 1/2 Mb resolution are possible using existing labeling and imaging technologies. The updated reconstruction code and the script files used for this paper are available at: https://github.com/heltilda/align3d.

A new reconstruction algorithm based on temporal neural network and its application in power quality disturbance data

Measurement and Control ◽

10.1177/00202940211019766 ◽

2021 ◽

pp. 002029402110197

Author(s):

Yan Liu ◽

Wei Tang ◽

Yiduo Luan

Keyword(s):

Neural Network ◽

Power Quality ◽

Large Scale ◽

Reconstruction Algorithms ◽

Reconstruction Accuracy ◽

Power Quality Disturbance ◽

Fully Connected ◽

Reconstruction Model ◽

Reconstruction Time

The traditional reconstruction algorithms based on p-norm, limited by their reconstruction model and data processing mode, are prone to reconstruction failure or long reconstruction time. In order to break through the limitations, this paper proposes a reconstruction algorithm based on the temporal neural network (TCN). A new reconstruction model based on TCN is first established, which does not need sparse representation and has large-scale parallel processing. Next, a TCN with a fully connected layer and symmetrical zero-padding operation is designed to meet the reconstruction requirements, including non-causality and length-inconsistency. Moreover, the proposed algorithm is constructed and applied to power quality disturbance (PQD) data. Experimental results show that the proposed algorithm can implement the reconstruction task, demonstrating better reconstruction accuracy and less reconstruction time than OMP, ROMP, CoSaMP, and SP. Therefore, the proposed algorithm is more attractive when dictionary design is complicated, or real-time reconstruction is required.

Neural Network for Large-Scale Problems, with Application to Human Motion

Journal of Computer Science Technology Updates ◽

10.15379/2410-2938.2016.03.02.02 ◽

2016 ◽

Vol 3 (2) ◽

Author(s):

Mohammad Bataineh

Keyword(s):

Neural Network ◽

Large Scale ◽

Human Motion ◽

Investigation of Improved Cooperative Coevolution for Large-Scale Global Optimization Problems

Algorithms ◽

10.3390/a14050146 ◽

2021 ◽

Vol 14 (5) ◽

pp. 146

Author(s):

Aleksei Vakhnin ◽

Evgenii Sopov

Keyword(s):

Global Optimization ◽

Evolutionary Algorithms ◽

Numerical Experiments ◽

Large Scale ◽

Optimization Problems ◽

State Of The Art ◽

Fixed Number ◽

High Dimensional ◽

Cooperative Coevolution ◽

Modern real-valued optimization problems are complex and high-dimensional, and they are known as “large-scale global optimization (LSGO)” problems. Classic evolutionary algorithms (EAs) perform poorly on this class of problems because of the curse of dimensionality. Cooperative Coevolution (CC) is a high-performed framework for performing the decomposition of large-scale problems into smaller and easier subproblems by grouping objective variables. The efficiency of CC strongly depends on the size of groups and the grouping approach. In this study, an improved CC (iCC) approach for solving LSGO problems has been proposed and investigated. iCC changes the number of variables in subcomponents dynamically during the optimization process. The SHADE algorithm is used as a subcomponent optimizer. We have investigated the performance of iCC-SHADE and CC-SHADE on fifteen problems from the LSGO CEC’13 benchmark set provided by the IEEE Congress of Evolutionary Computation. The results of numerical experiments have shown that iCC-SHADE outperforms, on average, CC-SHADE with a fixed number of subcomponents. Also, we have compared iCC-SHADE with some state-of-the-art LSGO metaheuristics. The experimental results have shown that the proposed algorithm is competitive with other efficient metaheuristics.

A fast implementation of the FETI-DP method: FETI-DP-RBS-LNA and applications on large scale problems with localized non-linearities

International Journal for Numerical Methods in Engineering ◽

10.1002/nme.1301 ◽

2005 ◽

Vol 63 (6) ◽

pp. 833-858 ◽

Cited By ~ 4

Author(s):

Jun Sun ◽

Pan Michaleris ◽

Anshul Gupta ◽

Padma Raghavan

Keyword(s):

Large Scale ◽

Large Scale Problems ◽

Fast Implementation

Fast Multipole Virtual Boundary Element - Least Square Method for Solving 2-D Elastic Problems

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.204-210.2196 ◽

2011 ◽

Vol 204-210 ◽

pp. 2196-2201

Author(s):

Yan Tao Jiang ◽

Si Tian Chen ◽

Cheng Hua Li

Keyword(s):

Boundary Element ◽

Large Scale ◽

Fast Multipole Method ◽

Main Idea ◽

Least Square Method ◽

Least Square ◽

Fast Multipole ◽

Large Scale Problems ◽

Virtual Boundary ◽

Virtual Boundary Element

In this paper, the fast multipole virtual boundary element - least square method (Fast Multipole VBE - LSM) is proposed and used to simulate 2-D elastic problems, which is based on the fast multipole method (FMM) and virtual boundary element - least square method (VBE - LSM).The main idea of the method is to change computational model by applying the FMM to conventional VBE - LSM. The memory and operations could be reduced to be of linear proportion to the degree of freedom (DOF) and large scale problems could be effectively solved on a common desktop with this method. Numerical results show that this method holds virtues of high feasibility, accuracy and efficiency. Moreover, the idea of this method can be generalized and extended in application.