Multiprobabilistic Venn predictors with logistic regression

Ilia Nouretdinov; Dmitry Devetyarov; Brian Burford; Stephane Camuzeaux; Aleksandra Gentry-Maharaj; Ali Tiss; Celia Smith; Zhiyuan Luo; Alexey Chervonenkis; Rachel Hallett; Volodya Vovk; Mike Waterfield; Rainer Cramer; John F. Timms; Ian Jacobs; Usha Menon; Alexander Gammerman

doi:10.1007/978-3-642-33412-2_23

Multiprobabilistic Venn predictors with logistic regression

Ilia Nouretdinov, Dmitry Devetyarov, Brian Burford, Stephane Camuzeaux, Aleksandra Gentry-Maharaj, Ali Tiss, Celia Smith, Zhiyuan Luo, Alexey Chervonenkis, Rachel Hallett, Volodya Vovk, Mike Waterfield, Rainer Cramer, John F. Timms, Ian Jacobs, Usha Menon, Alexander Gammerman

Biochemistry and Molecular Biology

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

7 Citations (Scopus)

Abstract

This paper describes the methodology of providing multiprobability predictions for proteomic mass spectrometry data. The methodology is based on a newly developed machine learning framework called Venn machines. They allow us to output a valid probability interval. We apply this methodology to mass spectrometry data sets in order to predict the diagnosis of heart disease and early diagnoses of ovarian cancer. The experiments show that probability intervals are valid and narrow. In addition, probability intervals were compared with the output of a corresponding probability predictor.

Original language	English
Title of host publication	Artificial Intelligence Applications and Innovations - AIAI 2012 International Workshops
Subtitle of host publication	AIAB, AIeIA, CISE, COPA, IIVC, ISQL, MHDW, and WADTMB, Proceedings
Pages	224-233
Number of pages	10
Edition	PART 2
DOIs	https://doi.org/10.1007/978-3-642-33412-2_23
Publication status	Published - 17 Dec 2012
Event	8th International Workshop on Artificial Intelligence Applications and Innovations, AIAI 2012: AIAB, AIeIA, CISE, COPA, IIVC, ISQL, MHDW, and WADTMB - Halkidiki, Greece Duration: 27 Sep 2012 → 30 Sep 2012

Publication series

Name	IFIP Advances in Information and Communication Technology
Number	PART 2
Volume	382 AICT
ISSN (Print)	1868-4238

Conference

Conference	8th International Workshop on Artificial Intelligence Applications and Innovations, AIAI 2012: AIAB, AIeIA, CISE, COPA, IIVC, ISQL, MHDW, and WADTMB
Country	Greece
City	Halkidiki
Period	27/09/12 → 30/09/12

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Cite this

Nouretdinov, I., Devetyarov, D., Burford, B., Camuzeaux, S., Gentry-Maharaj, A., Tiss, A., Smith, C., Luo, Z., Chervonenkis, A., Hallett, R., Vovk, V., Waterfield, M., Cramer, R., Timms, J. F., Jacobs, I., Menon, U., & Gammerman, A. (2012). Multiprobabilistic Venn predictors with logistic regression. In Artificial Intelligence Applications and Innovations - AIAI 2012 International Workshops: AIAB, AIeIA, CISE, COPA, IIVC, ISQL, MHDW, and WADTMB, Proceedings (PART 2 ed., pp. 224-233). (IFIP Advances in Information and Communication Technology; Vol. 382 AICT, No. PART 2). https://doi.org/10.1007/978-3-642-33412-2_23

Nouretdinov, Ilia ; Devetyarov, Dmitry ; Burford, Brian ; Camuzeaux, Stephane ; Gentry-Maharaj, Aleksandra ; Tiss, Ali ; Smith, Celia ; Luo, Zhiyuan ; Chervonenkis, Alexey ; Hallett, Rachel ; Vovk, Volodya ; Waterfield, Mike ; Cramer, Rainer ; Timms, John F. ; Jacobs, Ian ; Menon, Usha ; Gammerman, Alexander. / Multiprobabilistic Venn predictors with logistic regression. Artificial Intelligence Applications and Innovations - AIAI 2012 International Workshops: AIAB, AIeIA, CISE, COPA, IIVC, ISQL, MHDW, and WADTMB, Proceedings. PART 2. ed. 2012. pp. 224-233 (IFIP Advances in Information and Communication Technology; PART 2).

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abstract = "This paper describes the methodology of providing multiprobability predictions for proteomic mass spectrometry data. The methodology is based on a newly developed machine learning framework called Venn machines. They allow us to output a valid probability interval. We apply this methodology to mass spectrometry data sets in order to predict the diagnosis of heart disease and early diagnoses of ovarian cancer. The experiments show that probability intervals are valid and narrow. In addition, probability intervals were compared with the output of a corresponding probability predictor.",

author = "Ilia Nouretdinov and Dmitry Devetyarov and Brian Burford and Stephane Camuzeaux and Aleksandra Gentry-Maharaj and Ali Tiss and Celia Smith and Zhiyuan Luo and Alexey Chervonenkis and Rachel Hallett and Volodya Vovk and Mike Waterfield and Rainer Cramer and Timms, {John F.} and Ian Jacobs and Usha Menon and Alexander Gammerman",

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note = "8th International Workshop on Artificial Intelligence Applications and Innovations, AIAI 2012: AIAB, AIeIA, CISE, COPA, IIVC, ISQL, MHDW, and WADTMB ; Conference date: 27-09-2012 Through 30-09-2012",

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Nouretdinov, I, Devetyarov, D, Burford, B, Camuzeaux, S, Gentry-Maharaj, A, Tiss, A, Smith, C, Luo, Z, Chervonenkis, A, Hallett, R, Vovk, V, Waterfield, M, Cramer, R, Timms, JF, Jacobs, I, Menon, U & Gammerman, A 2012, Multiprobabilistic Venn predictors with logistic regression. in Artificial Intelligence Applications and Innovations - AIAI 2012 International Workshops: AIAB, AIeIA, CISE, COPA, IIVC, ISQL, MHDW, and WADTMB, Proceedings. PART 2 edn, IFIP Advances in Information and Communication Technology, no. PART 2, vol. 382 AICT, pp. 224-233, 8th International Workshop on Artificial Intelligence Applications and Innovations, AIAI 2012: AIAB, AIeIA, CISE, COPA, IIVC, ISQL, MHDW, and WADTMB, Halkidiki, Greece, 27/09/12. https://doi.org/10.1007/978-3-642-33412-2_23

Multiprobabilistic Venn predictors with logistic regression. / Nouretdinov, Ilia; Devetyarov, Dmitry; Burford, Brian; Camuzeaux, Stephane; Gentry-Maharaj, Aleksandra; Tiss, Ali; Smith, Celia; Luo, Zhiyuan; Chervonenkis, Alexey; Hallett, Rachel; Vovk, Volodya; Waterfield, Mike; Cramer, Rainer; Timms, John F.; Jacobs, Ian; Menon, Usha; Gammerman, Alexander.

Artificial Intelligence Applications and Innovations - AIAI 2012 International Workshops: AIAB, AIeIA, CISE, COPA, IIVC, ISQL, MHDW, and WADTMB, Proceedings. PART 2. ed. 2012. p. 224-233 (IFIP Advances in Information and Communication Technology; Vol. 382 AICT, No. PART 2).

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

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T1 - Multiprobabilistic Venn predictors with logistic regression

AU - Nouretdinov, Ilia

AU - Devetyarov, Dmitry

AU - Burford, Brian

AU - Camuzeaux, Stephane

AU - Gentry-Maharaj, Aleksandra

AU - Tiss, Ali

AU - Smith, Celia

AU - Luo, Zhiyuan

AU - Chervonenkis, Alexey

AU - Hallett, Rachel

AU - Vovk, Volodya

AU - Waterfield, Mike

AU - Cramer, Rainer

AU - Timms, John F.

AU - Jacobs, Ian

AU - Menon, Usha

AU - Gammerman, Alexander

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AB - This paper describes the methodology of providing multiprobability predictions for proteomic mass spectrometry data. The methodology is based on a newly developed machine learning framework called Venn machines. They allow us to output a valid probability interval. We apply this methodology to mass spectrometry data sets in order to predict the diagnosis of heart disease and early diagnoses of ovarian cancer. The experiments show that probability intervals are valid and narrow. In addition, probability intervals were compared with the output of a corresponding probability predictor.

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DO - 10.1007/978-3-642-33412-2_23

M3 - Conference contribution

AN - SCOPUS:84870900277

SN - 9783642334115

T3 - IFIP Advances in Information and Communication Technology

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BT - Artificial Intelligence Applications and Innovations - AIAI 2012 International Workshops

T2 - 8th International Workshop on Artificial Intelligence Applications and Innovations, AIAI 2012: AIAB, AIeIA, CISE, COPA, IIVC, ISQL, MHDW, and WADTMB

Y2 - 27 September 2012 through 30 September 2012

ER -

Nouretdinov I, Devetyarov D, Burford B, Camuzeaux S, Gentry-Maharaj A, Tiss A et al. Multiprobabilistic Venn predictors with logistic regression. In Artificial Intelligence Applications and Innovations - AIAI 2012 International Workshops: AIAB, AIeIA, CISE, COPA, IIVC, ISQL, MHDW, and WADTMB, Proceedings. PART 2 ed. 2012. p. 224-233. (IFIP Advances in Information and Communication Technology; PART 2). https://doi.org/10.1007/978-3-642-33412-2_23

Access to Document

10.1007/978-3-642-33412-2_23

Link to publication in Scopus