Profiling of mass spectrometry data for ovarian cancer detection using negative correlation learning

Shan HE; Huanhuan CHEN; Xiaoli LI; Xin YAO

doi:10.1007/978-3-642-04277-5_19

Profiling of mass spectrometry data for ovarian cancer detection using negative correlation learning

Shan HE
, Huanhuan CHEN
, Xiaoli LI
, Xin YAO

Research output: Book Chapters | Papers in Conference Proceedings › Conference paper (refereed) › Research › peer-review

Abstract

This paper proposes a novel Mass Spectrometry data profiling method for ovarian cancer detection based on negative correlation learning (NCL). A modified Smoothed Nonlinear Energy Operator (SNEO) and correlation-based peak selection were applied to detected informative peaks for NCL to build a prediction model. In order to evaluate the performance of this novel method without bias, we employed randomization techniques by dividing the data set into testing set and training set to test the whole procedure for many times over. The classification performance of the proposed approach compared favorably with six machine learning algorithms. © 2009 Springer Berlin Heidelberg.

Original language	English
Title of host publication	Artificial Neural Networks : ICANN 2009 19th International Conference, Limassol, Cyprus, September 14-17, 2009, Proceedings, Part II
Editors	Cesare APLIPPI, Marios POLYCARPOU, Christos PANAYIOTOU, Georgios ELLINAS
Publisher	Springer Berlin Heidelberg
Pages	185-194
Number of pages	10
ISBN (Electronic)	9783642042775
ISBN (Print)	9783642042768
DOIs	https://doi.org/10.1007/978-3-642-04277-5_19
Publication status	Published - 2009
Externally published	Yes
Event	19th International Conference on Artificial Neural Networks, ICANN 2009 - Limassol, Cyprus Duration: 14 Sept 2009 → 17 Sept 2009

Publication series

Name	Lecture Notes in Computer Science
Publisher	Springer Berlin, Heidelberg
Volume	5769
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	19th International Conference on Artificial Neural Networks, ICANN 2009
Country/Territory	Cyprus
City	Limassol
Period	14/09/09 → 17/09/09

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Keywords

Bioinformatics
Data mining
Negative correlation learning
Proteomics

Access to Document

10.1007/978-3-642-04277-5_19

Cite this

HE, S., CHEN, H., LI, X., & YAO, X. (2009). Profiling of mass spectrometry data for ovarian cancer detection using negative correlation learning. In C. APLIPPI, M. POLYCARPOU, C. PANAYIOTOU, & G. ELLINAS (Eds.), Artificial Neural Networks : ICANN 2009 19th International Conference, Limassol, Cyprus, September 14-17, 2009, Proceedings, Part II (pp. 185-194). (Lecture Notes in Computer Science; Vol. 5769). Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-642-04277-5_19

HE, Shan ; CHEN, Huanhuan ; LI, Xiaoli et al. / Profiling of mass spectrometry data for ovarian cancer detection using negative correlation learning. Artificial Neural Networks : ICANN 2009 19th International Conference, Limassol, Cyprus, September 14-17, 2009, Proceedings, Part II. editor / Cesare APLIPPI ; Marios POLYCARPOU ; Christos PANAYIOTOU ; Georgios ELLINAS. Springer Berlin Heidelberg, 2009. pp. 185-194 (Lecture Notes in Computer Science).

@inproceedings{4bef977a2257492bb121501608d5891e,

title = "Profiling of mass spectrometry data for ovarian cancer detection using negative correlation learning",

abstract = "This paper proposes a novel Mass Spectrometry data profiling method for ovarian cancer detection based on negative correlation learning (NCL). A modified Smoothed Nonlinear Energy Operator (SNEO) and correlation-based peak selection were applied to detected informative peaks for NCL to build a prediction model. In order to evaluate the performance of this novel method without bias, we employed randomization techniques by dividing the data set into testing set and training set to test the whole procedure for many times over. The classification performance of the proposed approach compared favorably with six machine learning algorithms. {\textcopyright} 2009 Springer Berlin Heidelberg.",

keywords = "Bioinformatics, Data mining, Negative correlation learning, Proteomics",

author = "Shan HE and Huanhuan CHEN and Xiaoli LI and Xin YAO",

year = "2009",

doi = "10.1007/978-3-642-04277-5\_19",

language = "English",

isbn = "9783642042768",

series = "Lecture Notes in Computer Science",

publisher = "Springer Berlin Heidelberg",

pages = "185--194",

editor = "Cesare APLIPPI and Marios POLYCARPOU and Christos PANAYIOTOU and Georgios ELLINAS",

booktitle = "Artificial Neural Networks : ICANN 2009 19th International Conference, Limassol, Cyprus, September 14-17, 2009, Proceedings, Part II",

note = "19th International Conference on Artificial Neural Networks, ICANN 2009 ; Conference date: 14-09-2009 Through 17-09-2009",

}

HE, S, CHEN, H, LI, X & YAO, X 2009, Profiling of mass spectrometry data for ovarian cancer detection using negative correlation learning. in C APLIPPI, M POLYCARPOU, C PANAYIOTOU & G ELLINAS (eds), Artificial Neural Networks : ICANN 2009 19th International Conference, Limassol, Cyprus, September 14-17, 2009, Proceedings, Part II. Lecture Notes in Computer Science, vol. 5769, Springer Berlin Heidelberg, pp. 185-194, 19th International Conference on Artificial Neural Networks, ICANN 2009, Limassol, Cyprus, 14/09/09. https://doi.org/10.1007/978-3-642-04277-5_19

Profiling of mass spectrometry data for ovarian cancer detection using negative correlation learning. / HE, Shan; CHEN, Huanhuan; LI, Xiaoli et al.
Artificial Neural Networks : ICANN 2009 19th International Conference, Limassol, Cyprus, September 14-17, 2009, Proceedings, Part II. ed. / Cesare APLIPPI; Marios POLYCARPOU; Christos PANAYIOTOU; Georgios ELLINAS. Springer Berlin Heidelberg, 2009. p. 185-194 (Lecture Notes in Computer Science; Vol. 5769).

Research output: Book Chapters | Papers in Conference Proceedings › Conference paper (refereed) › Research › peer-review

TY - GEN

T1 - Profiling of mass spectrometry data for ovarian cancer detection using negative correlation learning

AU - HE, Shan

AU - CHEN, Huanhuan

AU - LI, Xiaoli

AU - YAO, Xin

PY - 2009

Y1 - 2009

N2 - This paper proposes a novel Mass Spectrometry data profiling method for ovarian cancer detection based on negative correlation learning (NCL). A modified Smoothed Nonlinear Energy Operator (SNEO) and correlation-based peak selection were applied to detected informative peaks for NCL to build a prediction model. In order to evaluate the performance of this novel method without bias, we employed randomization techniques by dividing the data set into testing set and training set to test the whole procedure for many times over. The classification performance of the proposed approach compared favorably with six machine learning algorithms. © 2009 Springer Berlin Heidelberg.

AB - This paper proposes a novel Mass Spectrometry data profiling method for ovarian cancer detection based on negative correlation learning (NCL). A modified Smoothed Nonlinear Energy Operator (SNEO) and correlation-based peak selection were applied to detected informative peaks for NCL to build a prediction model. In order to evaluate the performance of this novel method without bias, we employed randomization techniques by dividing the data set into testing set and training set to test the whole procedure for many times over. The classification performance of the proposed approach compared favorably with six machine learning algorithms. © 2009 Springer Berlin Heidelberg.

KW - Bioinformatics

KW - Data mining

KW - Negative correlation learning

KW - Proteomics

UR - https://www.scopus.com/pages/publications/70450186403

U2 - 10.1007/978-3-642-04277-5_19

DO - 10.1007/978-3-642-04277-5_19

M3 - Conference paper (refereed)

SN - 9783642042768

T3 - Lecture Notes in Computer Science

SP - 185

EP - 194

BT - Artificial Neural Networks : ICANN 2009 19th International Conference, Limassol, Cyprus, September 14-17, 2009, Proceedings, Part II

A2 - APLIPPI, Cesare

A2 - POLYCARPOU, Marios

A2 - PANAYIOTOU, Christos

A2 - ELLINAS, Georgios

PB - Springer Berlin Heidelberg

T2 - 19th International Conference on Artificial Neural Networks, ICANN 2009

Y2 - 14 September 2009 through 17 September 2009

ER -

HE S, CHEN H, LI X, YAO X. Profiling of mass spectrometry data for ovarian cancer detection using negative correlation learning. In APLIPPI C, POLYCARPOU M, PANAYIOTOU C, ELLINAS G, editors, Artificial Neural Networks : ICANN 2009 19th International Conference, Limassol, Cyprus, September 14-17, 2009, Proceedings, Part II. Springer Berlin Heidelberg. 2009. p. 185-194. (Lecture Notes in Computer Science). doi: 10.1007/978-3-642-04277-5_19

Profiling of mass spectrometry data for ovarian cancer detection using negative correlation learning

Abstract

Publication series

Conference

UN SDGs

Keywords

Access to Document

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