scholarly journals MeShClust2: Application of alignment-free identity scores in clustering long DNA sequences

2018 ◽  
Author(s):  
Benjamin T. James ◽  
Hani Z. Girgis
Keyword(s):  
Dna Sequences ◽  
Mean Shift ◽  
Training Data ◽  
Alignment Algorithm ◽  
Bacterial Genomes ◽  
Mean Shift Algorithm ◽  
Alignment Algorithms ◽  
Synthetic Sequence ◽  
The Mean ◽  
First Time

ABSTRACTGrouping sequences into similar clusters is an important part of sequence analysis. Widely used clustering tools sacrifice quality for speed. Previously, we developed MeShClust, which utilizes k-mer counts in an alignment-assisted classifier and the mean-shift algorithm for clustering DNA sequences. Although MeShClust outperformed related tools in terms of cluster quality, the alignment algorithm used for generating training data for the classifier was not scalable to longer sequences. In contrast, MeShClust2 generates semi-synthetic sequence pairs with known mutation rates, avoiding alignment algorithms. MeShClust2clustered 3600 bacterial genomes, providing a utility for clustering long sequences using identity scores for the first time.

scholarly journals MeShClust: an intelligent tool for clustering DNA sequences

2017 ◽  
Author(s):  
Benjamin T. James ◽  
Brian B. Luczak ◽  
Hani Z. Girgis
Keyword(s):  
Machine Learning ◽  
Dna Sequences ◽  
Learning Algorithm ◽  
Sequence Similarity ◽  
Mean Shift ◽  
Supervised Machine Learning ◽  
Global Alignment ◽  
Mean Shift Algorithm ◽  
Sequence Clustering ◽  
The Mean

ABSTRACTSequence clustering is a fundamental step in analyzing DNA sequences. Widely-used software tools for sequence clustering utilize greedy approaches that are not guaranteed to produce the best results. These tools are sensitive to one parameter that determines the similarity among sequences in a cluster. Often times, a biologist may not know the exact sequence similarity. Therefore, clusters produced by these tools do not likely match the real clusters comprising the data if the provided parameter is inaccurate. To overcome this limitation, we adapted the mean shift algorithm, an unsupervised machine-learning algorithm, which has been used successfully thousands of times in fields such as image processing and computer vision. The theory behind the mean shift algorithm, unlike the greedy approaches, guarantees convergence to the modes, e.g. cluster centers. Here we describe the first application of the mean shift algorithm to clustering DNA sequences. MeShClust is one of few applications of the mean shift algorithm in bioinformatics. Further, we applied supervised machine learning to predict the identity score produced by global alignment using alignment-free methods. We demonstrate MeShClust’s ability to cluster DNA sequences with high accuracy even when the sequence similarity parameter provided by the user is not very accurate.

The mean shift algorithm and its relation to kernel regression

2016 ◽  
Vol 348 ◽  
pp. 198-208 ◽  
Author(s):  
Youness Aliyari Ghassabeh ◽  
Frank Rudzicz
Keyword(s):  
Mean Shift ◽  
Kernel Regression ◽  
Mean Shift Algorithm ◽  
The Mean

Tracking algorithm of snowboard target in intelligent system

2020 ◽  
pp. 1-9
Author(s):  
Zhipeng Li ◽  
Xiaolan Li ◽  
Ming Shi ◽  
Wenli Song ◽  
Guowei Zhao ◽  
...  
Keyword(s):  
Target Tracking ◽  
High Speed ◽  
Intelligent System ◽  
Mean Shift ◽  
Target Position ◽  
Tracking Algorithm ◽  
Tracking Accuracy ◽  
Mean Shift Algorithm ◽  
Target Track ◽  
The Mean

Snowboarding is a kind of sport that takes snowboarding as a tool, swivels and glides rapidly on the specified slope line, and completes all kinds of difficult actions in the air. Because the sport is in the state of high-speed movement, it is difficult to direct guidance during the sport, which is not conducive to athletes to find problems and correct them, so it is necessary to track the target track of snowboarding. The target tracking algorithm is the main solution to this task, but there are many problems in the existing target tracking algorithm that have not been solved, especially the target tracking accuracy in complex scenes is insufficient. Therefore, based on the advantages of the mean shift algorithm and Kalman algorithm, this paper proposes a better tracking algorithm for snowboard moving targets. In the method designed in this paper, in order to solve the problem, a multi-algorithm fusion target tracking algorithm is proposed. Firstly, the SIFT feature algorithm is used for rough matching to determine the fuzzy position of the target. Then, the good performance of the mean shift algorithm is used to further match the target position and determine the exact position of the target. Finally, the Kalman filtering algorithm is used to further improve the target tracking algorithm to solve the template trajectory prediction under occlusion and achieve the target trajectory tracking algorithm design of snowboarding.

AN AUTONOMOUS SYSTEM FOR GROUND-BASED CLOUD TRACKING USING THE MEAN-SHIFT ALGORITHM

2011 ◽  
Author(s):  
M.D. O’Toole ◽  
S.A. Wormald ◽  
D. Kerr ◽  
J. Coupland ◽  
A.P. Sandford
Keyword(s):  
Autonomous System ◽  
Mean Shift ◽  
Mean Shift Algorithm ◽  
The Mean ◽  
Cloud Tracking

Research on Face Tracking in Community Monitoring System

2014 ◽  
Vol 484-485 ◽  
pp. 358-362
Author(s):  
Shuang Liu
Keyword(s):  
Target Tracking ◽  
Skin Color ◽  
Mean Shift ◽  
Texture Features ◽  
Mean Shift Algorithm ◽  
Local Character ◽  
Community Monitoring ◽  
The Mean ◽  
Target Block ◽  
Time Update

This paper proposes a block Mean-Shift algorithm based on target real-time update and LBP texture features, through the target update improves the accuracy of target tracking, enhances the local character of the target through the target block, so as to improve the robustness of algorithm based on skin color backgrounds. And then analyze the Mean-Shift algorithm cannot recover quickly lost target tracking defects, and its improvement by combining the frame difference method.

scholarly journals Thalamus Segmentation from Diffusion Tensor Magnetic Resonance Imaging

2007 ◽  
Vol 2007 ◽  
pp. 1-5 ◽  
Author(s):  
Ye Duan ◽  
Xiaoling Li ◽  
Yongjian Xi
Keyword(s):  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
Diffusion Tensor ◽  
Mean Shift ◽  
Fixed Number ◽  
Resonance Imaging ◽  
Mean Shift Algorithm ◽  
Nuclei Segmentation ◽  
Segmentation Algorithms ◽  
The Mean

We propose a semi-automatic thalamus and thalamus nuclei segmentation algorithm from Diffusion Tensor Magnetic Resonance Imaging (DT-MRI) based on the mean-shift algorithm. Comparing with existing thalamus segmentation algorithms which are mainly based on K-means algorithm, our mean-shift based algorithm is more flexible and adaptive. It does not assume a Gaussian distribution or a fixed number of clusters. Furthermore, the single parameter in the mean-shift based algorithm supports hierarchical clustering naturally.

scholarly journals Motion Capture and Intelligent Correction Method of Badminton Movement Based on Machine Vision

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Yibo Zhang ◽  
Jianjun Tang ◽  
Hui Huang
Keyword(s):  
Machine Vision ◽  
Target Tracking ◽  
Motion Capture ◽  
Mean Shift ◽  
Weak Links ◽  
Bottom Line ◽  
Mean Shift Algorithm ◽  
The Mean ◽  
Combined Algorithm ◽  
Improved Algorithm

In recent years, badminton has become more and more popular in national fitness programs. Amateur badminton clubs have been established all over the country, and amateur badminton events at all levels have increased significantly. Due to the lack of correct medical supervision and health guidance, many people have varying degrees of injury during sports. Therefore, it is very important to study the method of badminton movement capture and intelligent correction based on machine vision to provide safe and effective exercise plan for amateur badminton enthusiasts. This article aims to study the methods of motion capture and intelligent correction of badminton. Aiming at the shortcoming of the mean shift algorithm that it is easy to lose the target when the target is occluded or the background is disturbed, this paper combines the mean shift algorithm with the Kalman filter algorithm and proposes an improvement to the combined algorithm. The improved algorithm is added to the calculation of the average speed of the target, which can be used as the target speed when the target is occluded to predict the area where the target may appear at the next moment, and it can also be used as a judgment condition for whether the target is interfered by the background. The improved algorithm combines the macroscopic motion information of the target, can overcome the problem of target loss when the target is occluded and background interference, and improves the robustness of target tracking. Using LabVIEW development environment to write the system software of the Japanese standard tracking robot, the experiment verified the rationality and correctness of the improved target tracking algorithm and motion control method, which can meet the real-time performance of moving target tracking. Experimental results show that 83% of amateur badminton players have problems with asymmetric functions and weak links. Based on machine vision technology, it can provide reliable bottom line reference for making training plans, effectively improve the quality of action, improve the efficiency of action, and promote the development of sports competitive level.

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