An accurate clustering algorithm for fast protein-profiling using SCICA on MALDI-TOF

Amit Acharyya, Mavuduru Neehar, Ganesh R. Naik

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Citation (Scopus)

Abstract

In this paper we propose an accurate clustering algorithm as the necessary step of the Single Channel Independent Component Analysis (SCICA) in the context of the fast extraction of protein profiles from the mass spectra (MALDI-TOF) data. In general K-means clustering is employed for clustering of the basis vectors. However given its iterative and statistical nature, convergence to the same clusters for the same data sets is not always guaranteed making it inaccurate, especially in protein-profiling where reliability of the bio-marker based disease detection and diagnosis depend immensely on the reliability of the clustering algorithm. Furthermore the proposed algorithm does not involve any arithmetic computations helping expedite the entire SCICA process.

Original languageEnglish
Title of host publication2015 IEEE International Symposium on Circuits and Systems (ISCAS), Lisbon, 2015
Place of PublicationLisbon
Pages69-72
Number of pages4
ISBN (Electronic)9781479983919
DOIs
Publication statusPublished - 30 Jul 2015
Externally publishedYes
EventIEEE International Symposium on Circuits and Systems, ISCAS 2015 - Lisbon, Portugal
Duration: 24 May 201527 May 2015

Conference

ConferenceIEEE International Symposium on Circuits and Systems, ISCAS 2015
CountryPortugal
CityLisbon
Period24/05/1527/05/15

Keywords

  • Clustering
  • K-Means
  • Protein profiling
  • SCICA

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