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Structural SCOP Superfamily Level Classification Using Unsupervised Machine Learning

Authors:

Ulavappa B. Angadi,

M. VenkatesuluAuthors Info & Claims

IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB), Volume 9, Issue 2

Pages 601 - 608

https://doi.org/10.1109/TCBB.2011.114

Published: 01 March 2012 Publication History

Abstract

One of the major research directions in bioinformatics is that of assigning superfamily classification to a given set of proteins. The classification reflects the structural, evolutionary, and functional relatedness. These relationships are embodied in a hierarchical classification, such as the Structural Classification of Protein (SCOP), which is mostly manually curated. Such a classification is essential for the structural and functional analyses of proteins. Yet a large number of proteins remain unclassified. In this study, we have proposed an unsupervised machine learning approach to classify and assign a given set of proteins to SCOP superfamilies. In the method, we have constructed a database and similarity matrix using P-values obtained from an all-against-all BLAST run and trained the network with the ART2 unsupervised learning algorithm using the rows of the similarity matrix as input vectors, enabling the trained network to classify the proteins from 0.82 to 0.97 f-measure accuracy. The performance of ART2 has been compared with that of spectral clustering, Random forest, SVM, and HHpred. ART2 performs better than the others except HHpred. HHpred performs better than ART2 and the sum of errors is smaller than that of the other methods evaluated.

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Cited By

Somase KImambi S(2021)Develop and implement unsupervised learning through hybrid FFPA clustering in large-scale datasetsSoft Computing - A Fusion of Foundations, Methodologies and Applications10.1007/s00500-020-05140-y25:1(277-290)Online publication date: 1-Jan-2021
https://dl.acm.org/doi/10.1007/s00500-020-05140-y
Li HGao CMa LSong Y(2015)An improved ART2 neural networkNeurocomputing10.1016/j.neucom.2014.12.055156:C(239-244)Online publication date: 25-May-2015
https://dl.acm.org/doi/10.1016/j.neucom.2014.12.055
Yuguang ZKai XDongyan S(2014)Clustering and group selection of interim product in shipbuildingJournal of Intelligent Manufacturing10.1007/s10845-013-0737-y25:6(1393-1401)Online publication date: 1-Dec-2014
https://dl.acm.org/doi/10.1007/s10845-013-0737-y

Structural SCOP Superfamily Level Classification Using Unsupervised Machine Learning
1. Applied computing
  1. Life and medical sciences
2. Computing methodologies
  1. Machine learning
    1. Learning paradigms
      1. Supervised learning
      2. Unsupervised learning
    2. Machine learning approaches

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cover image IEEE/ACM Transactions on Computational Biology and Bioinformatics

IEEE/ACM Transactions on Computational Biology and Bioinformatics Volume 9, Issue 2

March 2012

316 pages

ISSN:1545-5963

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IEEE Computer Society Press

Washington, DC, United States

Publication History

Published: 01 March 2012

Published in TCBB Volume 9, Issue 2

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Cited By

Somase KImambi S(2021)Develop and implement unsupervised learning through hybrid FFPA clustering in large-scale datasetsSoft Computing - A Fusion of Foundations, Methodologies and Applications10.1007/s00500-020-05140-y25:1(277-290)Online publication date: 1-Jan-2021
https://dl.acm.org/doi/10.1007/s00500-020-05140-y
Li HGao CMa LSong Y(2015)An improved ART2 neural networkNeurocomputing10.1016/j.neucom.2014.12.055156:C(239-244)Online publication date: 25-May-2015
https://dl.acm.org/doi/10.1016/j.neucom.2014.12.055
Yuguang ZKai XDongyan S(2014)Clustering and group selection of interim product in shipbuildingJournal of Intelligent Manufacturing10.1007/s10845-013-0737-y25:6(1393-1401)Online publication date: 1-Dec-2014
https://dl.acm.org/doi/10.1007/s10845-013-0737-y

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