Qualified predictions for proteomics pattern diagnostics with confidence machines

Zhiyuan Luo; Tony Bellotti; Alex Gammerman

doi:10.1007/978-3-540-28651-6_7

Qualified predictions for proteomics pattern diagnostics with confidence machines

Zhiyuan Luo, Tony Bellotti, Alex Gammerman

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

6 Citations (Scopus)

Abstract

In this paper, we focus on the problem of prediction with confidence and describe the recently developed transductive confidence machines (TCM). TCM allows us to make predictions within predefined confidence levels, thus providing a controlled and calibrated classification environment. We apply the TCM to the problem of proteomics pattern diagnostics. We demonstrate that the TCM performs well, yielding accurate, well-calibrated and informative predictions in both online and offline learning settings.

Original language	English
Title of host publication	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Editors	Zheng Rong Yang, Richard Everson, Hujun Yin
Publisher	Springer Verlag
Pages	46-51
Number of pages	6
ISBN (Print)	3540228810, 9783540228813
DOIs	https://doi.org/10.1007/978-3-540-28651-6_7
Publication status	Published - 2004
Externally published	Yes

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	3177
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

ASJC Scopus subject areas

Theoretical Computer Science
General Computer Science

UN SDGs

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

Access to Document

10.1007/978-3-540-28651-6_7

Cite this

Luo, Z., Bellotti, T., & Gammerman, A. (2004). Qualified predictions for proteomics pattern diagnostics with confidence machines. In Z. R. Yang, R. Everson, & H. Yin (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 46-51). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 3177). Springer Verlag. https://doi.org/10.1007/978-3-540-28651-6_7

Luo, Zhiyuan ; Bellotti, Tony ; Gammerman, Alex. / Qualified predictions for proteomics pattern diagnostics with confidence machines. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). editor / Zheng Rong Yang ; Richard Everson ; Hujun Yin. Springer Verlag, 2004. pp. 46-51 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inbook{9ee9eed9e87d4e68a4de87b6047cc35e,

title = "Qualified predictions for proteomics pattern diagnostics with confidence machines",

abstract = "In this paper, we focus on the problem of prediction with confidence and describe the recently developed transductive confidence machines (TCM). TCM allows us to make predictions within predefined confidence levels, thus providing a controlled and calibrated classification environment. We apply the TCM to the problem of proteomics pattern diagnostics. We demonstrate that the TCM performs well, yielding accurate, well-calibrated and informative predictions in both online and offline learning settings.",

author = "Zhiyuan Luo and Tony Bellotti and Alex Gammerman",

year = "2004",

doi = "10.1007/978-3-540-28651-6_7",

language = "English",

isbn = "3540228810",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer Verlag",

pages = "46--51",

editor = "Yang, {Zheng Rong} and Richard Everson and Hujun Yin",

booktitle = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

address = "Germany",

}

Luo, Z, Bellotti, T & Gammerman, A 2004, Qualified predictions for proteomics pattern diagnostics with confidence machines. in ZR Yang, R Everson & H Yin (eds), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 3177, Springer Verlag, pp. 46-51. https://doi.org/10.1007/978-3-540-28651-6_7

Qualified predictions for proteomics pattern diagnostics with confidence machines. / Luo, Zhiyuan; Bellotti, Tony; Gammerman, Alex.
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). ed. / Zheng Rong Yang; Richard Everson; Hujun Yin. Springer Verlag, 2004. p. 46-51 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 3177).

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

TY - CHAP

T1 - Qualified predictions for proteomics pattern diagnostics with confidence machines

AU - Luo, Zhiyuan

AU - Bellotti, Tony

AU - Gammerman, Alex

PY - 2004

Y1 - 2004

N2 - In this paper, we focus on the problem of prediction with confidence and describe the recently developed transductive confidence machines (TCM). TCM allows us to make predictions within predefined confidence levels, thus providing a controlled and calibrated classification environment. We apply the TCM to the problem of proteomics pattern diagnostics. We demonstrate that the TCM performs well, yielding accurate, well-calibrated and informative predictions in both online and offline learning settings.

AB - In this paper, we focus on the problem of prediction with confidence and describe the recently developed transductive confidence machines (TCM). TCM allows us to make predictions within predefined confidence levels, thus providing a controlled and calibrated classification environment. We apply the TCM to the problem of proteomics pattern diagnostics. We demonstrate that the TCM performs well, yielding accurate, well-calibrated and informative predictions in both online and offline learning settings.

UR - http://www.scopus.com/inward/record.url?scp=33746268245&partnerID=8YFLogxK

U2 - 10.1007/978-3-540-28651-6_7

DO - 10.1007/978-3-540-28651-6_7

M3 - Book Chapter

AN - SCOPUS:33746268245

SN - 3540228810

SN - 9783540228813

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 46

EP - 51

BT - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

A2 - Yang, Zheng Rong

A2 - Everson, Richard

A2 - Yin, Hujun

PB - Springer Verlag

ER -

Luo Z, Bellotti T, Gammerman A. Qualified predictions for proteomics pattern diagnostics with confidence machines. In Yang ZR, Everson R, Yin H, editors, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Springer Verlag. 2004. p. 46-51. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-540-28651-6_7

Qualified predictions for proteomics pattern diagnostics with confidence machines

Abstract

Publication series

ASJC Scopus subject areas

UN SDGs

Access to Document

Other files and links

Cite this