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

Amit Acharyya; Mavuduru Neehar; Ganesh R. Naik

doi:10.1109/ISCAS.2015.7168572

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 proceeding › Conference contribution › peer-review

4 Citations (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 language	English
Title of host publication	2015 IEEE International Symposium on Circuits and Systems (ISCAS), Lisbon, 2015
Place of Publication	Lisbon
Pages	69-72
Number of pages	4
ISBN (Electronic)	9781479983919
DOIs	https://doi.org/10.1109/ISCAS.2015.7168572
Publication status	Published - 30 Jul 2015
Externally published	Yes
Event	IEEE International Symposium on Circuits and Systems, ISCAS 2015 - Lisbon, Portugal Duration: 24 May 2015 → 27 May 2015

Conference

Conference	IEEE International Symposium on Circuits and Systems, ISCAS 2015
Country/Territory	Portugal
City	Lisbon
Period	24/05/15 → 27/05/15

Keywords

Clustering
K-Means
Protein profiling
SCICA

Access to Document

10.1109/ISCAS.2015.7168572Licence: In Copyright

Cite this

@inproceedings{461ee116e24546ceb10a507a89428e48,

title = "An accurate clustering algorithm for fast protein-profiling using SCICA on MALDI-TOF",

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.",

keywords = "Clustering, K-Means, Protein profiling, SCICA",

author = "Amit Acharyya and Mavuduru Neehar and Naik, {Ganesh R.}",

year = "2015",

month = jul,

day = "30",

doi = "10.1109/ISCAS.2015.7168572",

language = "English",

pages = "69--72",

booktitle = "2015 IEEE International Symposium on Circuits and Systems (ISCAS), Lisbon, 2015",

note = "IEEE International Symposium on Circuits and Systems, ISCAS 2015 ; Conference date: 24-05-2015 Through 27-05-2015",

}

TY - GEN

T1 - An accurate clustering algorithm for fast protein-profiling using SCICA on MALDI-TOF

AU - Acharyya, Amit

AU - Neehar, Mavuduru

AU - Naik, Ganesh R.

PY - 2015/7/30

Y1 - 2015/7/30

N2 - 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.

AB - 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.

KW - Clustering

KW - K-Means

KW - Protein profiling

KW - SCICA

UR - http://www.scopus.com/inward/record.url?scp=84946232443&partnerID=8YFLogxK

U2 - 10.1109/ISCAS.2015.7168572

DO - 10.1109/ISCAS.2015.7168572

M3 - Conference contribution

AN - SCOPUS:84946232443

SP - 69

EP - 72

BT - 2015 IEEE International Symposium on Circuits and Systems (ISCAS), Lisbon, 2015

CY - Lisbon

T2 - IEEE International Symposium on Circuits and Systems, ISCAS 2015

Y2 - 24 May 2015 through 27 May 2015

ER -

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

Abstract

Conference

Keywords

Access to Document

Other files and links

Fingerprint

Cite this