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Spectral clustering on neighborhood kernels with modified symmetry for remote homology detection

Sarkar, Anasua LU orcid ; Nikolski, Macha and Maulik, Ujjwal (2011) 2nd International Conference on Emerging Applications of Information Technology, EAIT 2011 p.269-272
Abstract

Remote homology detction among proteins in an unsupervised approach from sequences is an important problem in computational biology. The existing neighborhood cluster kernel methods and Markov clustering algorithms are most efficient for homolog detection. Yet they deviate from random walks with inflation or similarity depending on hard thresholds. Our spectral clustering approach with new combined local alignment kernels more effectively exploits state-ofthe- art neighborhood vectors globally. This appoarch combined with Markov clustering similarity after modified symmetry based corrections outperforms other six cluster kernels for unsupervised remote homolog detection even in multi-domain and promiscuous proteins from Genolevures... (More)

Remote homology detction among proteins in an unsupervised approach from sequences is an important problem in computational biology. The existing neighborhood cluster kernel methods and Markov clustering algorithms are most efficient for homolog detection. Yet they deviate from random walks with inflation or similarity depending on hard thresholds. Our spectral clustering approach with new combined local alignment kernels more effectively exploits state-ofthe- art neighborhood vectors globally. This appoarch combined with Markov clustering similarity after modified symmetry based corrections outperforms other six cluster kernels for unsupervised remote homolog detection even in multi-domain and promiscuous proteins from Genolevures database with better biological relevance. Source code available upon request.

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Please use this url to cite or link to this publication:
author
; and
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
keywords
Kernel matrix, Modified symmetry distance measure, Remote homology detection, Spectral clustering
host publication
Proceedings - 2nd International Conference on Emerging Applications of Information Technology, EAIT 2011
article number
5734942
pages
4 pages
conference name
2nd International Conference on Emerging Applications of Information Technology, EAIT 2011
conference location
Kolkata, India
conference dates
2011-02-19 - 2011-02-20
external identifiers
  • scopus:79953880257
ISBN
9780769543291
DOI
10.1109/EAIT.2011.81
language
English
LU publication?
no
id
41e6b4ab-e517-47a6-9930-7b0c3093bc5f
date added to LUP
2018-10-09 09:57:00
date last changed
2022-01-31 05:58:12
@inproceedings{41e6b4ab-e517-47a6-9930-7b0c3093bc5f,
  abstract     = {{<p>Remote homology detction among proteins in an unsupervised approach from sequences is an important problem in computational biology. The existing neighborhood cluster kernel methods and Markov clustering algorithms are most efficient for homolog detection. Yet they deviate from random walks with inflation or similarity depending on hard thresholds. Our spectral clustering approach with new combined local alignment kernels more effectively exploits state-ofthe- art neighborhood vectors globally. This appoarch combined with Markov clustering similarity after modified symmetry based corrections outperforms other six cluster kernels for unsupervised remote homolog detection even in multi-domain and promiscuous proteins from Genolevures database with better biological relevance. Source code available upon request.</p>}},
  author       = {{Sarkar, Anasua and Nikolski, Macha and Maulik, Ujjwal}},
  booktitle    = {{Proceedings - 2nd International Conference on Emerging Applications of Information Technology, EAIT 2011}},
  isbn         = {{9780769543291}},
  keywords     = {{Kernel matrix; Modified symmetry distance measure; Remote homology detection; Spectral clustering}},
  language     = {{eng}},
  month        = {{04}},
  pages        = {{269--272}},
  title        = {{Spectral clustering on neighborhood kernels with modified symmetry for remote homology detection}},
  url          = {{http://dx.doi.org/10.1109/EAIT.2011.81}},
  doi          = {{10.1109/EAIT.2011.81}},
  year         = {{2011}},
}