Quantifying overfitting potential in drug binding datasets

Brian Davis; Kevin Mcloughlin; Jonathan Allen; Sally R. Ellingson

doi:10.1007/978-3-030-50420-5_44

Quantifying overfitting potential in drug binding datasets

Brian Davis, Kevin Mcloughlin, Jonathan Allen, Sally R. Ellingson

Internal Medicine

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

2 Scopus citations

Abstract

In this paper, we investigate potential biases in datasets used to make drug binding predictions using machine learning. We investigate a recently published metric called the Asymmetric Validation Embedding (AVE) bias which is used to quantify this bias and detect overfitting. We compare it to a slightly revised version and introduce a new weighted metric. We find that the new metrics allow to quantify overfitting while not overly limiting training data and produce models with greater predictive value.

Original language	English
Title of host publication	Computational Science – ICCS 2020 - 20th International Conference, Proceedings
Editors	Valeria V. Krzhizhanovskaya, Gábor Závodszky, Michael H. Lees, Peter M.A. Sloot, Peter M.A. Sloot, Peter M.A. Sloot, Jack J. Dongarra, Sérgio Brissos, João Teixeira
Pages	585-598
Number of pages	14
DOIs	https://doi.org/10.1007/978-3-030-50420-5_44
State	Published - 2020
Event	20th International Conference on Computational Science, ICCS 2020 - Amsterdam, Netherlands Duration: Jun 3 2020 → Jun 5 2020

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	12139 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	20th International Conference on Computational Science, ICCS 2020
Country/Territory	Netherlands
City	Amsterdam
Period	6/3/20 → 6/5/20

Bibliographical note

Publisher Copyright:
© Springer Nature Switzerland AG 2020.

Funding

Supported by Lawrence Livermore National Laboratory and the University of Kentucky Markey Cancer Center.

Funders	Funder number
University of Kentucky Markey Comprehensive Cancer Center

Keywords

Data overfitting
Drug discovery
Machine learning

ASJC Scopus subject areas

Theoretical Computer Science
General Computer Science

Access to Document

10.1007/978-3-030-50420-5_44

Cite this

Davis, B., Mcloughlin, K., Allen, J., & Ellingson, S. R. (2020). Quantifying overfitting potential in drug binding datasets. In V. V. Krzhizhanovskaya, G. Závodszky, M. H. Lees, P. M. A. Sloot, P. M. A. Sloot, P. M. A. Sloot, J. J. Dongarra, S. Brissos, & J. Teixeira (Eds.), Computational Science – ICCS 2020 - 20th International Conference, Proceedings (pp. 585-598). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12139 LNCS). https://doi.org/10.1007/978-3-030-50420-5_44

Davis, Brian ; Mcloughlin, Kevin ; Allen, Jonathan et al. / Quantifying overfitting potential in drug binding datasets. Computational Science – ICCS 2020 - 20th International Conference, Proceedings. editor / Valeria V. Krzhizhanovskaya ; Gábor Závodszky ; Michael H. Lees ; Peter M.A. Sloot ; Peter M.A. Sloot ; Peter M.A. Sloot ; Jack J. Dongarra ; Sérgio Brissos ; João Teixeira. 2020. pp. 585-598 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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abstract = "In this paper, we investigate potential biases in datasets used to make drug binding predictions using machine learning. We investigate a recently published metric called the Asymmetric Validation Embedding (AVE) bias which is used to quantify this bias and detect overfitting. We compare it to a slightly revised version and introduce a new weighted metric. We find that the new metrics allow to quantify overfitting while not overly limiting training data and produce models with greater predictive value.",

keywords = "Data overfitting, Drug discovery, Machine learning",

author = "Brian Davis and Kevin Mcloughlin and Jonathan Allen and Ellingson, \{Sally R.\}",

note = "Publisher Copyright: {\textcopyright} Springer Nature Switzerland AG 2020.; 20th International Conference on Computational Science, ICCS 2020 ; Conference date: 03-06-2020 Through 05-06-2020",

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editor = "Krzhizhanovskaya, \{Valeria V.\} and G{\'a}bor Z{\'a}vodszky and Lees, \{Michael H.\} and Sloot, \{Peter M.A.\} and Sloot, \{Peter M.A.\} and Sloot, \{Peter M.A.\} and Dongarra, \{Jack J.\} and S{\'e}rgio Brissos and Jo{\~a}o Teixeira",

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Davis, B, Mcloughlin, K, Allen, J & Ellingson, SR 2020, Quantifying overfitting potential in drug binding datasets. in VV Krzhizhanovskaya, G Závodszky, MH Lees, PMA Sloot, PMA Sloot, PMA Sloot, JJ Dongarra, S Brissos & J Teixeira (eds), Computational Science – ICCS 2020 - 20th International Conference, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 12139 LNCS, pp. 585-598, 20th International Conference on Computational Science, ICCS 2020, Amsterdam, Netherlands, 6/3/20. https://doi.org/10.1007/978-3-030-50420-5_44

Quantifying overfitting potential in drug binding datasets. / Davis, Brian; Mcloughlin, Kevin; Allen, Jonathan et al.
Computational Science – ICCS 2020 - 20th International Conference, Proceedings. ed. / Valeria V. Krzhizhanovskaya; Gábor Závodszky; Michael H. Lees; Peter M.A. Sloot; Peter M.A. Sloot; Peter M.A. Sloot; Jack J. Dongarra; Sérgio Brissos; João Teixeira. 2020. p. 585-598 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12139 LNCS).

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

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AU - Allen, Jonathan

AU - Ellingson, Sally R.

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AB - In this paper, we investigate potential biases in datasets used to make drug binding predictions using machine learning. We investigate a recently published metric called the Asymmetric Validation Embedding (AVE) bias which is used to quantify this bias and detect overfitting. We compare it to a slightly revised version and introduce a new weighted metric. We find that the new metrics allow to quantify overfitting while not overly limiting training data and produce models with greater predictive value.

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KW - Drug discovery

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BT - Computational Science – ICCS 2020 - 20th International Conference, Proceedings

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Davis B, Mcloughlin K, Allen J, Ellingson SR. Quantifying overfitting potential in drug binding datasets. In Krzhizhanovskaya VV, Závodszky G, Lees MH, Sloot PMA, Sloot PMA, Sloot PMA, Dongarra JJ, Brissos S, Teixeira J, editors, Computational Science – ICCS 2020 - 20th International Conference, Proceedings. 2020. p. 585-598. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-50420-5_44

Quantifying overfitting potential in drug binding datasets

Abstract

Publication series

Conference

Bibliographical note

Funding

Keywords

ASJC Scopus subject areas

Access to Document

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