Probabilistic sequence clustering with spectral learning

2014 ◽  
Vol 29 ◽  
pp. 1-19 ◽  
Author(s):  
Y. Cem Sübakan ◽  
Barış Kurt ◽  
A. Taylan Cemgil ◽  
Bülent Sankur
Keyword(s):  
Sequence Clustering ◽  
Spectral Learning

Self-spectral learning with GAN based spectral-spatial target detection for hyperspectral image

2021 ◽  
Author(s):  
Weiying Xie ◽  
Jiaqing Zhang ◽  
Jie Lei ◽  
Yunsong Li ◽  
Xiuping Jia
Keyword(s):  
Target Detection ◽  
Hyperspectral Image ◽  
Spectral Learning

Sequence clustering in bioinformatics: an empirical study

2018 ◽  
Author(s):  
Quan Zou ◽  
Gang Lin ◽  
Xingpeng Jiang ◽  
Xiangrong Liu ◽  
Xiangxiang Zeng
Keyword(s):  
Empirical Study ◽  
Sequence Clustering

scholarly journals Use of nrdA gene sequence clustering to estimate the prevalence of different Achromobacter species among Cystic Fibrosis patients in the UK

2016 ◽  
Vol 15 (4) ◽  
pp. 479-485 ◽  
Author(s):  
Amy Coward ◽  
Dervla T.D. Kenna ◽  
Claire Perry ◽  
Kate Martin ◽  
Michel Doumith ◽  
...  
Keyword(s):  
Cystic Fibrosis ◽  
Gene Sequence ◽  
Sequence Clustering ◽  
The Uk

Basis selection in spectral learning of predictive state representations

2018 ◽  
Vol 310 ◽  
pp. 183-189 ◽  
Author(s):  
Chunqing Huang ◽  
Yisheng An ◽  
Sun Zhou ◽  
Zhezheng Hong ◽  
Yunlong Liu
Keyword(s):  
Spectral Learning ◽  
Predictive State Representations

EdClust: A heuristic sequence clustering method with higher sensitivity

2021 ◽  
Author(s):  
Ming Cao ◽  
Qinke Peng ◽  
Ze-Gang Wei ◽  
Fei Liu ◽  
Yi-Fan Hou
Keyword(s):  
Large Scale ◽  
Sequence Data ◽  
Clustering Algorithms ◽  
Clustering Methods ◽  
Sequencing Data ◽  
Clustering Method ◽  
Cluster Number ◽  
Sequence Clustering ◽  
Downstream Analysis ◽  
Heuristic Clustering

The development of high-throughput technologies has produced increasing amounts of sequence data and an increasing need for efficient clustering algorithms that can process massive volumes of sequencing data for downstream analysis. Heuristic clustering methods are widely applied for sequence clustering because of their low computational complexity. Although numerous heuristic clustering methods have been developed, they suffer from two limitations: overestimation of inferred clusters and low clustering sensitivity. To address these issues, we present a new sequence clustering method (edClust) based on Edlib, a C/C[Formula: see text] library for fast, exact semi-global sequence alignment to group similar sequences. The new method edClust was tested on three large-scale sequence databases, and we compared edClust to several classic heuristic clustering methods, such as UCLUST, CD-HIT, and VSEARCH. Evaluations based on the metrics of cluster number and seed sensitivity (SS) demonstrate that edClust can produce fewer clusters than other methods and that its SS is higher than that of other methods. The source codes of edClust are available from https://github.com/zhang134/EdClust.git under the GNU GPL license.

The Parallel Maximal Cliques Algorithm for Protein Sequence Clustering

2009 ◽  
Vol 6 (7) ◽  
pp. 1368-1372 ◽  
Author(s):  
Khalid Jaber ◽  
Nur'Aini Abdul Ras ◽  
Rosni Abdullah
Keyword(s):  
Protein Sequence ◽  
Sequence Clustering
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