Particle swarm optimization for analysis of mass spectral serum profiles

H. Ressom; R. S. Varghese; D. Saha; E. Orvisky; L. Goldman; E. F. Petricoin; T. P. Conrads; T. D. Veenstra; M. Abdel-Hamid; C. A. Loffredo; R. Goldman

doi:10.1145/1068009.1068078

Particle swarm optimization for analysis of mass spectral serum profiles

H. Ressom^*, R. S. Varghese, D. Saha, E. Orvisky, L. Goldman, E. F. Petricoin, T. P. Conrads, T. D. Veenstra, M. Abdel-Hamid, C. A. Loffredo, R. Goldman

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

7 Scopus citations

Abstract

Serum profiling using mass spectrometry is an emerging technology with a great potential to provide biomarkers for complex diseases such as cancer. However, protein profiles obtained from current mass spectrometric technologies are characterized by their high dimensionality and complex spectra with substantial level of noise. These characteristics have generated challenges in discovery of proteins and protein-profiles that distinguish cancer patients from healthy individuals. This paper proposes a novel machine learning method that combines support vector machines with particle swarm optimization for biomarker discovery. Prior to applying the proposed biomarker selection algorithm, low-level analysis methods are used for smoothing, baseline correction, normalization, and peak detection. The proposed method is applied for biomarker discovery from serum mass spectral profiles of liver cancer patients and controls.

Original language	English
Title of host publication	GECCO 2005 - Genetic and Evolutionary Computation Conference
Editors	H.G. Beyer, U.M. O'Reilly, D. Arnold, W. Banzhaf, C. Blum, E.W. Bonabeau, E. Cantu-Paz, D. Dasgupta, K. Deb, al et al
Pages	431-438
Number of pages	8
DOIs	https://doi.org/10.1145/1068009.1068078
State	Published - 2005
Externally published	Yes
Event	GECCO 2005 - Genetic and Evolutionary Computation Conference - Washington, D.C., United States Duration: 25 Jun 2005 → 29 Jun 2005

Publication series

Name	GECCO 2005 - Genetic and Evolutionary Computation Conference

Conference

Conference	GECCO 2005 - Genetic and Evolutionary Computation Conference
Country/Territory	United States
City	Washington, D.C.
Period	25/06/05 → 29/06/05

Keywords

Proteomics
Support vector machines
Swarm intelligence

Access to Document

10.1145/1068009.1068078

Cite this

Ressom, H., Varghese, R. S., Saha, D., Orvisky, E., Goldman, L., Petricoin, E. F., Conrads, T. P., Veenstra, T. D., Abdel-Hamid, M., Loffredo, C. A., & Goldman, R. (2005). Particle swarm optimization for analysis of mass spectral serum profiles. In H. G. Beyer, U. M. O'Reilly, D. Arnold, W. Banzhaf, C. Blum, E. W. Bonabeau, E. Cantu-Paz, D. Dasgupta, K. Deb, & A. et al (Eds.), GECCO 2005 - Genetic and Evolutionary Computation Conference (pp. 431-438). (GECCO 2005 - Genetic and Evolutionary Computation Conference). https://doi.org/10.1145/1068009.1068078

Ressom, H. ; Varghese, R. S. ; Saha, D. et al. / Particle swarm optimization for analysis of mass spectral serum profiles. GECCO 2005 - Genetic and Evolutionary Computation Conference. editor / H.G. Beyer ; U.M. O'Reilly ; D. Arnold ; W. Banzhaf ; C. Blum ; E.W. Bonabeau ; E. Cantu-Paz ; D. Dasgupta ; K. Deb ; al et al. 2005. pp. 431-438 (GECCO 2005 - Genetic and Evolutionary Computation Conference).

@inproceedings{0681157ca2f443cdbe590d1ad46c831b,

title = "Particle swarm optimization for analysis of mass spectral serum profiles",

abstract = "Serum profiling using mass spectrometry is an emerging technology with a great potential to provide biomarkers for complex diseases such as cancer. However, protein profiles obtained from current mass spectrometric technologies are characterized by their high dimensionality and complex spectra with substantial level of noise. These characteristics have generated challenges in discovery of proteins and protein-profiles that distinguish cancer patients from healthy individuals. This paper proposes a novel machine learning method that combines support vector machines with particle swarm optimization for biomarker discovery. Prior to applying the proposed biomarker selection algorithm, low-level analysis methods are used for smoothing, baseline correction, normalization, and peak detection. The proposed method is applied for biomarker discovery from serum mass spectral profiles of liver cancer patients and controls.",

keywords = "Proteomics, Support vector machines, Swarm intelligence",

author = "H. Ressom and Varghese, {R. S.} and D. Saha and E. Orvisky and L. Goldman and Petricoin, {E. F.} and Conrads, {T. P.} and Veenstra, {T. D.} and M. Abdel-Hamid and Loffredo, {C. A.} and R. Goldman",

year = "2005",

doi = "10.1145/1068009.1068078",

language = "English",

isbn = "1595930108",

series = "GECCO 2005 - Genetic and Evolutionary Computation Conference",

pages = "431--438",

editor = "H.G. Beyer and U.M. O'Reilly and D. Arnold and W. Banzhaf and C. Blum and E.W. Bonabeau and E. Cantu-Paz and D. Dasgupta and K. Deb and {et al}, al",

booktitle = "GECCO 2005 - Genetic and Evolutionary Computation Conference",

note = "GECCO 2005 - Genetic and Evolutionary Computation Conference ; Conference date: 25-06-2005 Through 29-06-2005",

}

Ressom, H, Varghese, RS, Saha, D, Orvisky, E, Goldman, L, Petricoin, EF , Conrads, TP, Veenstra, TD, Abdel-Hamid, M, Loffredo, CA & Goldman, R 2005, Particle swarm optimization for analysis of mass spectral serum profiles. in HG Beyer, UM O'Reilly, D Arnold, W Banzhaf, C Blum, EW Bonabeau, E Cantu-Paz, D Dasgupta, K Deb & A et al (eds), GECCO 2005 - Genetic and Evolutionary Computation Conference. GECCO 2005 - Genetic and Evolutionary Computation Conference, pp. 431-438, GECCO 2005 - Genetic and Evolutionary Computation Conference, Washington, D.C., United States, 25/06/05. https://doi.org/10.1145/1068009.1068078

Particle swarm optimization for analysis of mass spectral serum profiles. / Ressom, H.; Varghese, R. S.; Saha, D. et al.
GECCO 2005 - Genetic and Evolutionary Computation Conference. ed. / H.G. Beyer; U.M. O'Reilly; D. Arnold; W. Banzhaf; C. Blum; E.W. Bonabeau; E. Cantu-Paz; D. Dasgupta; K. Deb; al et al. 2005. p. 431-438 (GECCO 2005 - Genetic and Evolutionary Computation Conference).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Particle swarm optimization for analysis of mass spectral serum profiles

AU - Ressom, H.

AU - Varghese, R. S.

AU - Saha, D.

AU - Orvisky, E.

AU - Goldman, L.

AU - Petricoin, E. F.

AU - Conrads, T. P.

AU - Veenstra, T. D.

AU - Abdel-Hamid, M.

AU - Loffredo, C. A.

AU - Goldman, R.

PY - 2005

Y1 - 2005

N2 - Serum profiling using mass spectrometry is an emerging technology with a great potential to provide biomarkers for complex diseases such as cancer. However, protein profiles obtained from current mass spectrometric technologies are characterized by their high dimensionality and complex spectra with substantial level of noise. These characteristics have generated challenges in discovery of proteins and protein-profiles that distinguish cancer patients from healthy individuals. This paper proposes a novel machine learning method that combines support vector machines with particle swarm optimization for biomarker discovery. Prior to applying the proposed biomarker selection algorithm, low-level analysis methods are used for smoothing, baseline correction, normalization, and peak detection. The proposed method is applied for biomarker discovery from serum mass spectral profiles of liver cancer patients and controls.

AB - Serum profiling using mass spectrometry is an emerging technology with a great potential to provide biomarkers for complex diseases such as cancer. However, protein profiles obtained from current mass spectrometric technologies are characterized by their high dimensionality and complex spectra with substantial level of noise. These characteristics have generated challenges in discovery of proteins and protein-profiles that distinguish cancer patients from healthy individuals. This paper proposes a novel machine learning method that combines support vector machines with particle swarm optimization for biomarker discovery. Prior to applying the proposed biomarker selection algorithm, low-level analysis methods are used for smoothing, baseline correction, normalization, and peak detection. The proposed method is applied for biomarker discovery from serum mass spectral profiles of liver cancer patients and controls.

KW - Proteomics

KW - Support vector machines

KW - Swarm intelligence

UR - http://www.scopus.com/inward/record.url?scp=27744545542&partnerID=8YFLogxK

U2 - 10.1145/1068009.1068078

DO - 10.1145/1068009.1068078

M3 - Conference contribution

AN - SCOPUS:27744545542

SN - 1595930108

SN - 9781595930101

T3 - GECCO 2005 - Genetic and Evolutionary Computation Conference

SP - 431

EP - 438

BT - GECCO 2005 - Genetic and Evolutionary Computation Conference

A2 - Beyer, H.G.

A2 - O'Reilly, U.M.

A2 - Arnold, D.

A2 - Banzhaf, W.

A2 - Blum, C.

A2 - Bonabeau, E.W.

A2 - Cantu-Paz, E.

A2 - Dasgupta, D.

A2 - Deb, K.

A2 - et al, al

T2 - GECCO 2005 - Genetic and Evolutionary Computation Conference

Y2 - 25 June 2005 through 29 June 2005

ER -

Ressom H, Varghese RS, Saha D, Orvisky E, Goldman L, Petricoin EF et al. Particle swarm optimization for analysis of mass spectral serum profiles. In Beyer HG, O'Reilly UM, Arnold D, Banzhaf W, Blum C, Bonabeau EW, Cantu-Paz E, Dasgupta D, Deb K, et al A, editors, GECCO 2005 - Genetic and Evolutionary Computation Conference. 2005. p. 431-438. (GECCO 2005 - Genetic and Evolutionary Computation Conference). doi: 10.1145/1068009.1068078

Particle swarm optimization for analysis of mass spectral serum profiles

Abstract

Publication series

Conference

Keywords

Access to Document

Fingerprint

Cite this